JP2003121351A - Decision method for freshness of meat - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a method in which the redness, the color deterioration ratio and the lipid oxidation level as indexes of the freshness of meat can be measured nondestructively in a short time. SOLUTION: The transmission-reflectance of stored beef is measured, the quadratic differential value X1 of log (1/transmittance-reflectance) at a wavelength of 620±20 nm, the quadratic differential value X2 of log (1/transmittance- reflectance) at a wavelength of 744±2 nm, and the quadratic differential value X3 of log (1/transmittance-reflectance) at a wavelength of 590±2 nm, are found, and the freshness of the stored beef is decided on the basis of the redness Y obtained by the following working curve; Y=K0 +K1 X1 +K2 X2 +K3 X3 (K0 =2.55 to 7.47, K1 =77.30 to 98.96, K2 =303.10 to 513.63, K3 =42.72 to 52.93).

Description

Detailed Description of the Invention

[0001]

TECHNICAL FIELD The present invention enables nondestructive and accurate measurement of fading / discoloration and lipid oxidation, which are important traits in the distribution and retail stages of meat (beef, pork, chicken), in a short time. And a method for easily determining the freshness of meat.

[0002]

2. Description of the Related Art Even when meat is stored in a refrigerator, the meat color fades with the passage of time and lipids oxidize to give off an oxidative odor. The freshness of meat during storage is currently judged by sight or smell, and more scientific evaluation methods are required. Among the traits of lightness (L *), flesh color, redness (a *), and yellowness (b *), redness has the largest change during storage. During the storage period, the redness is significantly lower than the yellowness, and the brightness is almost unchanged. Therefore, it can be said that the redness is a good indicator of the freshness of meat, like the metmyoglobin formation rate and the lipid oxidation degree.

It is reported that consumers will stop purchasing when the rate of metmyoglobin formation on the beef surface reaches 30-40% (Greene, BE, Hsin, I. and Zipser, MW).
(1971) J. Food. Sci. 36: 940-942.). A general taste evaluator can detect the oxidized odor of meat when the TBARS value is 0.
6 to 2.0 mg MDA / kg meat range (Greene,
BE and Cumuze, TH (1981) J. Food Sci. 47: 52
-54, 58.) and TBAR for experienced tasters
It has been reported that an oxidative odor can be detected in the S value range of 0.5 to 1.0 mg MDA / kg meat (Tarladgis, BG,
Watts, BM, Younathan, MT and Dugan, L. (1960)
J. Amer. Oil Chem. Soc. 37: 44-48.). Furthermore, consumers are reported to be unable to detect an oxidative odor at TBARS values below about 0.5 mg MDA / kg meat (Gray, J.
I. and Pearson, AM (1987) in Advances in Meat R
esearch, Vol. 3., Restructured meat and poultry pr
oducts, Ed by AM Pearson and TR Dutson. Van
Nostrand Reinhold Co, New York, p. 221.).

[0004]

The objective measurement of lipid oxidation requires treatments such as mixing of meat with a reagent, crushing, filtration, reaction time, and measurement with a spectrophotometer, and is too time consuming. Since many meats are inspected at the meat distribution / sales site, it is necessary to be able to accurately determine the freshness in a short time in a non-destructive manner.

The measurement of flesh color and metmyoglobin formation rate requires analysis with a highly accurate spectrophotometer and an integrating sphere detector. For analysis of lipid oxidation degree, trichloroacetic acid (20%, 50 mL), a powerful drug, was used to remove meat (20.0%).
g) is crushed and made up to a fixed amount (100 mL),
After filtration, the filtrate is allowed to react with another reagent (tetraethoxypropane) for a long time (16 hours) in the dark and measured with a spectrophotometer at 53
It is necessary to measure the absorbance at 2 nm and at the same time prepare a calibration curve with a standard sample to determine a 2-thiobarbituric acid reactive substance (TBARS) value representing oxidation of lipid.

On the other hand, near infrared light (about 700 to 2500 nm)
(Wavelength region of) is applied to the substance, an absorption spectrum consisting of vibration bands peculiar to the substance structure (functional group) is obtained. Utilizing this property, the absorption spectrum and the physicochemical composition of a substance can be related by a regression equation. In recent years, near-infrared spectrophotometers have been developed with the rapid processing of spectral data by computers, enabling rapid and nondestructive analysis of food and feed ingredients (Osborne and Fe
arn, 1986). As a test before the present invention, the present inventors have
A quality grading method for beef carcass was developed, which was characterized by measuring the transmissivity of the longissimus thoracic muscle on the incised surface of beef carcass, and rating the quality based on the fat content obtained by a calibration curve (Patent No. 3060059). Therefore, an object of the present invention is to develop and provide a method for nondestructively measuring redness, fading rate, and lipid oxidation degree, which are indicators of freshness of meat, in a short time (about 10 seconds).

[0007]

[Means for Solving the Problems] The transmission reflectance of many meats (beef, pork, chicken) during storage at 400 to 1100 nm was measured by a near infrared spectrophotometer and expressed as log (1 / transmission reflectance). It was recorded and its log-transformed value was secondarily differentiated in order to reduce the non-uniform effect of cut meat. The redness (a *), which represents the change in freshness of meat during storage, the rate of metmyoglobin formation, the degree of lipid oxidation (2-thiobarbituric acid reactive substance: TBARS), which is the best indicator of the freshness of meat during storage Was analyzed in the laboratory by standard methods. A calibration curve that best represents the redness of meat, the rate of metmyoglobin formation, and the degree of lipid oxidation was created from near-infrared spectroscopic values using multiple regression equations, and the freshness of various meats was non-destructive at the distribution and retail stages. It is possible to measure in a short time.

According to the method for determining freshness of meat according to the present invention, the transmission reflectance of beef during storage is measured and the second derivative of log (1 / transmission reflectance) at a wavelength of 620 ± 2 nm, X1, wavelength 7
Redness degree obtained by the following calibration curve by obtaining the secondary differential value X2 of log (1 / transmissive reflectance) at 44 ± 2 nm and the secondary differential value X3 of log (1 / transmissive reflectance) at a wavelength of 590 ± 2 nm. It is characterized in that the freshness of the beef during storage is judged based on Y. Y = K0 + K1X1 + K2X2 + K3X3 (K0 = 2.55 to 7.47, K1 = 77.30 to 9)
8.96, K2 = 303.10-513.63, K3 =
42.72 to 52.93)

Another aspect of the method for determining freshness of meat according to the present invention is to measure the transmission reflectance of beef during storage to measure a wavelength of 624.
Second derivative of log (1 / transmissivity) at ± 2 nm
1. Determining the freshness of the beef in storage based on the metmyoglobin formation rate Y obtained by the following calibration curve by obtaining the second derivative X2 of the log (1 / transmissivity / reflectance) at a wavelength of 1020 ± 2 nm. Characterize. Y = K0 + K1X1 + K2X2 (K0 = 42.24 to 77.33, K1 = -225.7)
9-218.18, K2 = 676.881-1673.
07)

Another aspect of the method for determining freshness of meat according to the present invention is to measure the transmission reflectance of beef during storage to measure a wavelength of 620.
Second derivative of log (1 / transmissivity) at ± 2 nm
1, log at wavelength 978 ± 2nm (1 / transmissivity)
Second-order derivative value X2 of log (1
It is characterized in that the freshness of the beef during storage is determined based on the lipid oxidation degree (TBARS) Y obtained by the following calibration curve. Y = K0 + K1X1 + K2X2 + K3X3 (K0 = 2.40 to 2.90, K1 = −9.78 to −
8.99, K2 = -17.77 to -16.42, K3 =
-53.52--30.78)

Another embodiment of the method for determining the freshness of meat according to the present invention is to measure the transmission reflectance of pork during storage to measure a wavelength of 588.
Second derivative of log (1 / transmissivity) at ± 2 nm
1, log at wavelength 688 ± 2 nm (1 / transmissivity)
Second-order derivative value X2 of log (1 at wavelength 556 ± 2 nm
/ Transmissivity / reflectance) second-order differential value X3, and the freshness of the stored pork is determined based on the redness Y obtained from the following calibration curve. Y = K0 + K1X1 + K2X2 + K3X3 (K0 = -1.77 to -0.44, K1 = -35.67
~ -26.95, K2 = 40.51 to 46.07, K3
= -16.59 to -15.57)

Another embodiment of the method for determining freshness of meat according to the present invention is that the transmission reflectance of pork during storage is measured to determine a wavelength of 616.
Second derivative of log (1 / transmissivity) at ± 2 nm
1, log at wavelength 694 ± 2 nm (1 / transmissivity)
Second-order differential value X2 of log (1 at wavelength 744 ± 2 nm
/ Transmissivity / reflectance) second-order differential value X3, and the freshness of the stored pork is determined based on the metmyoglobin formation rate Y obtained by the following calibration curve. Y = K0 + K1X1 + K2X2 + K3X3 (K0 = 51.28 to 61.10, K1 = −204.2)
2--191.43, K2 = 381.67-445.5.
5, K3 = 613.28 to 1901.96)

Another embodiment of the method for determining freshness of meat according to the present invention is to measure the transmission reflectance of pork during storage to measure the wavelength 604.
Second derivative of log (1 / transmissivity) at ± 2 nm
1, log at wavelength 934 ± 2 nm (1 / transmissivity)
Second derivative value X2 of the log (1 at wavelength 480 ± 2 nm
/ Transmissivity / reflectance) second derivative X3, wavelength 748 ± 2 nm
The second derivative X4 of log (1 / transmissivity / reflectance) at is calculated, and the lipid oxidation degree (TBAR obtained by the following calibration curve is obtained.
S) The freshness of the stored pork is judged based on Y. Y = K0 + K1X1 + K2X2 + K3X3 + K4X4 (K0 = 1.05 to 1.21, K1 = −2.53 to −
2.31, K2 = -6.41 to -3.95, K3 =-
0.42 to -0.11, K4 = 10.04 to 19.8
5)

Another embodiment of the method for determining the freshness of meat according to the present invention is to measure the transmission reflectance of chicken meat during storage to measure a wavelength of 576.
Second derivative of log (1 / transmissivity) at ± 2 nm
1, log at wavelength 462 ± 2 nm (1 / transmissivity)
Second derivative of X2, log at wavelength 1018 ± 2 nm
It is characterized in that the second-order differential value X3 of (1 / transmissivity / reflectance) is obtained, and the freshness of the chicken during storage is determined based on the redness degree Y obtained by the following calibration curve. Y = K0 + K1X1 + K2X2 + K3X3 (K0 = 3.87 to 4.73, K1 = −31.48 to −
18.47, K2 = -14.70 to -12.43, K3
= 167.13 to 395.84)

Another embodiment of the method for determining freshness of meat according to the present invention is to measure the transmission reflectance of chicken meat during storage to measure the wavelength 618.
Second derivative of log (1 / transmissivity) at ± 2 nm
1, log at wavelength 998 ± 2 nm (1 / transmissivity)
Second-order differential value X2 of log (1 at wavelength 832 ± 2 nm
/ Transmissivity / reflectance) second-order differential value X3 is determined, and the freshness of the chicken meat during storage is determined based on the metmyoglobin formation rate Y obtained by the following calibration curve. Y = K0 + K1X1 + K2X2 + K3X3 (K0 = 30.31 to 38.76, K1 = -343.0
8 to -278.65, K2 = -1065.73 to -89.
5.98, K3 = -5067.40 to -3753.5
0)

Another embodiment of the method for judging the freshness of meat according to the present invention is to measure the transmission reflectance of chicken meat during storage to measure the wavelength 626.
Second derivative of log (1 / transmissivity) at ± 2 nm
1, log at wavelength 552 ± 2 nm (1 / transmissivity)
Second-order differential value X2 of log (1 at wavelength 832 ± 2 nm
/ Transmissivity / reflectance) second-order differential value X3 is determined, and the freshness of the chicken meat during storage is determined based on the lipid oxidation degree (TBARS) Y obtained by the following calibration curve. Y = K0 + K1X1 + K2X2 + K3X3 (K0 = −1.43 to 0.29, K1 = −22.34 to
-17.11, K2 = -15.55 to -9.53, K3
= 392.02 to 456.21)

From the beef samples used in the present invention, the redness (a
*) And metmyoglobin formation ratio (a * =-
0.1924 x metmyoglobin formation rate + 21.89
6) and the relational expression (a * = − 4.2276 × TBARS + 18.948) between the redness (a *) and the TBARS value.
6) was calculated. Is it beef? 30% which is the standard value of the fading rate
Substituting the metmyoglobin formation rate of
* Becomes 16.1, and? The standard value for lipid oxidation is 0.6
Substituting TBARS for mg MDA / kg meat into the latter equation yields a * of 16.4. From these relational expressions, it was estimated that consumers would stop purchasing when the redness (a *) of beef became lower than 16.

From the pork sample used in the present invention, the relational expression (a * =-3.15) between redness (a *) and TBARS value.
08 × TBARS + 7.4327) and the relational expression between metmyoglobin formation rate and TBARS value (metmyoglobin formation rate = 54.791 × TBARS + 28.2)
25) was calculated. Substituting the standard value of lipid oxidation of 0.6 mg MDA / kg meat TBARS into these relational expressions, a * was 5.5, and the metmyoglobin formation ratio was 6
It becomes 1.1%. From these relational expressions, if the redness (a *) of pork becomes lower than 6, consumers will stop buying,
It was also estimated that consumers would stop purchasing when the rate of metmyoglobin formation in pork was higher than 60%.

Furthermore, the relational expression (a * =-0.9) between the redness (a *) and the TBARS value from the chicken sample used in the present invention.
33 × TBARS + 3.7368) and a relational expression between the ratio of metmyoglobin formation and the TBARS value (formation ratio of metmyoglobin = 6.6561 × TBARS + 48.2).
99) was calculated. Substitution of 0.6 mg MDA / kg meat TBARS, which is the standard value of lipid oxidation, into these relational expressions gave a * of 3.2, and the metmyoglobin formation ratio was 5
It becomes 2.3%. From these relational expressions, if the redness (a *) of chicken becomes lower than 3, consumers will stop purchasing,
It was also estimated that consumers would stop buying when the metmyoglobin formation rate of chicken was higher than 50%.

Therefore, in the above-mentioned standard value at which consumers will not purchase meat, that is, in beef, the redness (a *) is 16, the metmyoglobin formation rate is 30%,
By applying the TBARS value of 0.6 mg MDA / kg meat, the redness (a *) in pork is 6, the metmyoglobin formation rate is 60%, and the TBARS value of 0.6 mg MDA / kg meat is applied. As a result, the redness (a *) of chicken was 3, the rate of metmyoglobin formation was 50%, and the TBARS value was 0.6 mgM.
By applying the standard of DA / kg meat, the change in redness of each meat and the degree of oxidation of meat pigment and lipid oxidation can be determined. Therefore, by measuring the redness of the meat, the ratio of metmyoglobin formation, and the degree of lipid oxidation by the method of this example, the freshness of various meats at the distribution and retail stages (a certain meat is suitable for sale, or suitable) Can be determined in a short time non-destructively.

[0021]

DETAILED DESCRIPTION OF THE INVENTION The contents of the present invention will be described below in detail with reference to the drawings.

The materials used were the semitendinosus muscle and the longissimus dorsi muscle of 6 Japanese Black steers, the biceps femoris and longissimus muscle of 12 LWD cross-fed pigs, and the superficial pectoralis muscle of 24 broilers. . Steak samples (2 cm thick, 1 cm thick from each muscle)
0.0 g) was prepared. Each sample was placed in a plastic weighing dish, covered with oxygen permeable polyvinyl chloride, and stored at 4 ° C. under a fluorescent lamp, simulating the display conditions of a normal meat store (hereinafter, this storage state is referred to as display). A near-infrared spectrophotometer (NIRSystems 5500) was equipped with an optical fiber for measuring meat cross-sections, and beef and pork were sampled 10 times after exhibition 1, 4, 7, 10 days, and chicken 4 days after exhibition (about 10 times). Sec), and the transmission reflectance (Re) at 400 to 1100 nm was measured and recorded as log (1 / Re).
The redness (a *) of the flesh color of the measured site and the brown-brown metmyoglobin formation rate indicating the oxidation of the flesh pigment and the 2-thiobarbituric acid reactive substance (TBAR) indicating the oxidation of the lipid
S) values were analyzed.

In order to reduce the non-uniform effect of cut meat, its logarithmic conversion value (log (1 / Re)) was second-order differentiated. Redness from near-infrared spectral values using the following multiple regression equation,
A calibration curve that best represents the ratio of metmyoglobin formation and the degree of lipid oxidation was prepared. As the procedure, first, a wavelength in which the single correlation coefficient or the multiple correlation coefficient is stable with respect to the fluctuation of the wavelength, and the coefficient (Kn) is also stable is searched for, and the wavelength belonging to the trait is determined. A maximum of 4 wavelengths was selected as a calibration curve model for each trait so as to prevent overfitting (overfitting) due to too many explanatory variables (number of wavelengths) being incorporated into the calibration curve. Next, the accuracy of each calibration curve model was evaluated by the cross-validation method. Then, the increase of the multiple correlation coefficient of the calibration curve model and the decrease of the prediction standard error by the cross-validation method were compared and examined, and the most accurate calibration curve was determined with the smallest explanatory variable (number of wavelengths).

Y = K0 + K1 (X1) + ... + Kn (Xn) where Y: meat redness, metmyoglobin formation rate or lipid oxidation degree K0-Kn: calibration curve coefficients X1-Xn: log (1) at each wavelength / Re) second derivative (n: 1 to maximum 4) Hereinafter, a method of deriving a calibration curve for various kinds of meat will be described in detail.

[Embodiment 1] Beef (1) Redness (a *) calibration curve Three kinds of wavelengths in the redness (a *) calibration curve of beef are selected as follows. FIG. 1A shows a
It is a figure which shows the single correlation coefficient of each wavelength with respect to *. From this figure, find the wavelength where the coefficient (K1) is stable in the wavelength region where the single correlation coefficient is stable with respect to the fluctuation of each wavelength,
620 nm was selected as the first wavelength (610-700).
nm is the red wavelength range). FIG. 1B is a diagram showing a multiple correlation coefficient between the second wavelength and the a * after the first wavelength is selected. From this figure, a wavelength in which the coefficient (K2) is stable is searched for in the wavelength region in which the multiple correlation coefficient is stable with respect to changes in each wavelength, and 744 nm is selected as the second wavelength. FIG.1 (c) is a figure which shows the multiple correlation coefficient of the 3rd wavelength and a * after selecting the 1st and 2nd wavelength. From this figure, a wavelength in which the coefficient (K3) is stable is searched for in the wavelength region in which the multiple correlation coefficient is stable with respect to fluctuations in each wavelength, and 590 nm is selected as the third wavelength. From the figure showing the multiple correlation coefficient of the fourth wavelength and a * after selecting the first, second and third wavelengths, the wavelength at which the multiple correlation coefficient is stable with respect to fluctuations of each wavelength In the area, coefficient (K4)
Also find a stable wavelength, 780nm as the fourth wavelength
Was selected.

Four calibration curve models (calibration curve using the first wavelength, which sequentially incorporates the above four wavelengths as explanatory variables)
Calibration curves using first and second wavelengths, first, second and third
For a calibration curve using wavelengths, a calibration curve using the first, second, third and fourth wavelengths), the accuracy evaluation is performed by the cross-validation method, and cross-validation is performed with an increase in the multiple correlation coefficient of the calibration curve model. The most accurate calibration curve with the fewest explanatory variables (number of wavelengths) was examined by comparing with the reduction of the standard error of prediction by the method. The results are shown in Table 1. As a result, a calibration curve using the above first, second and third wavelengths was selected.

[0027]

[Table 1]

In FIG. 2, the vertical axis represents the measured value by the method of this embodiment, and the horizontal axis represents the analytical value by the conventional method, in which the measurement points of 48 samples are plotted. as a result,
The multiple correlation coefficient (R) between both measured values was 0.963, and the standard error (SEC) was 1.14, showing a very high correlation.

Here, for example, the first wavelength is 618n
m, second wavelength 742 nm, third wavelength 588
When nm is selected, the multiple correlation coefficient of this calibration curve is 0.96.
1, the standard error is 1.18, and the first wavelength is 622.
nm, the second wavelength is 746 nm, and the third wavelength is 59
When 2 nm is selected, the multiple correlation coefficient of this calibration curve is 0.9
64, and the standard error is 1.13. As described above, even if the first wavelength, the second wavelength, and the third wavelength are changed in the range of ± 2 nm, substantially the same high correlation can be obtained, and the coefficients are K0 = 2.55 to 7.47 and K1. = 77.30-
98.96, K2 = 303.10-513.63, K3
= 42.72 to 52.93, a multiple correlation coefficient of 0.961 to 0.964 and a standard error of 1.13 to
A sufficiently high correlation of 1.18 can be obtained.

By using the calibration curve of the redness (a *) of beef thus obtained, it is possible to accurately and nondestructively measure the change in the redness of beef in the distribution and retail stages in a short time. A decrease in redness indicates a decrease in freshness. Therefore, if the redness measured by the method of the present invention is 16 or less, the freshness of beef is low, and if the redness is 16 or more, the freshness is good. Can be determined.

(2) Calibration curve for the ratio of metmyoglobin formation The two wavelengths in the calibration curve for the ratio of metmyoglobin formation of beef are selected as follows. Figure 3
(A) is a figure showing the single correlation coefficient of each wavelength with respect to the metmyoglobin formation rate. From this figure, find a wavelength in which the coefficient (K1) is stable in the wavelength region where the single correlation coefficient is stable with respect to fluctuations of each wavelength, and a wavelength close to the maximum absorption wavelength (630 nm) of metmyoglobin. Seeking
624 nm was selected as the first wavelength. Figure 3 (b) shows
It is a figure which shows the multiple correlation coefficient of the 2nd wavelength after selecting the 1st wavelength, and the metmyoglobin formation rate. From this figure, in the wavelength region where the multiple correlation coefficient is stable with respect to fluctuations of each wavelength, a wavelength having a stable coefficient (K2) is searched for, and 1020 nm is selected as the second wavelength (1020 nm is a protein Is the wavelength at which myoglobin is a chromoprotein). From the figure showing the multiple correlation coefficient between the third wavelength after selecting the first and second wavelengths and the metmyoglobin formation ratio, the wavelength region in which the multiple correlation coefficient is stable with respect to fluctuations in each wavelength Then, look for a wavelength with a stable coefficient (K3),
698 nm was selected as the third wavelength. From the figure showing the multiple correlation coefficient between the fourth wavelength and the metmyoglobin formation rate after selecting the first, second and third wavelengths, it can be seen that the multiple correlation coefficient is stable with respect to variations in each wavelength. In the existing wavelength range, search for a wavelength with a stable coefficient (K4), and set the fourth wavelength to 5
88 nm was selected.

Four calibration curve models (calibration curve using the first wavelength,
Calibration curves using first and second wavelengths, first, second and third
For a calibration curve using wavelengths, a calibration curve using the first, second, third and fourth wavelengths), the accuracy evaluation is performed by the cross-validation method, and cross-validation is performed with an increase in the multiple correlation coefficient of the calibration curve model. The most accurate calibration curve with the fewest explanatory variables (number of wavelengths) was examined by comparing with the reduction of the standard error of prediction by the method. The results are shown in Table 2. As a result, the above first and second
A calibration curve using wavelength was selected.

[0033]

[Table 2]

In FIG. 4, the vertical axis represents the measured value by the method of this embodiment, and the horizontal axis represents the analytical value by the conventional method, in which the measurement points of 48 samples are plotted. as a result,
The multiple correlation coefficient (R) between both measured values was 0.989, and the standard error (SEC) was 3.06%, showing a very high correlation.

Here, for example, the first wavelength is 622n
m, and 1018 nm is selected as the second wavelength, the multiple correlation coefficient of this calibration curve is 0.990, and the standard error is 2.96%.
Therefore, the first wavelength is 626 nm and the second wavelength is 1
When 022 nm is selected, the multiple correlation coefficient of this calibration curve is 0.986 and the standard error is 3.42%. In this way, even if the first wavelength and the second wavelength are both changed within a range of ± 2 nm, almost the same high correlation can be obtained, and the coefficients are K0 = 42.24 to 77.33 and K1 = -225.
79 to -218.18, K2 = 676.881 to 167.
If a value within the range of 3.07 is used, the multiple correlation coefficient is 0.98.
A sufficiently high correlation of 6 to 0.990 and standard error of 2.96 to 3.42% can be obtained.

By using the calibration curve of the rate of metmyoglobin formation in beef thus obtained, the rate of metmyoglobin formation in beef can be accurately measured in a short time in a non-destructive manner in the distribution and retail stages. Since an increase in the metmyoglobin formation rate indicates a decrease in freshness, if the metmyoglobin formation rate measured by the method of the present invention is 30% or more, it means that the freshness of the beef is low, and the metmyoglobin formation rate is 30% or less. If so, it can be determined that the freshness is good.

(3) Calibration curve of lipid oxidation degree (TBARS) Three kinds of wavelengths in the calibration curve of beef lipid oxidation degree (TBARS) are selected as follows. Figure 5
(A) is a figure showing the single correlation coefficient of each wavelength with respect to a TBARS value. From this figure, we searched for a wavelength in which the coefficient (K1) was stable in the wavelength region where the single correlation coefficient was stable with respect to changes in each wavelength, and selected 620 nm as the first wavelength (beef redness And the wavelength of metmyoglobin formation were both selected in the same region as the first wavelength (this is probably because the three traits have a high correlation with each other). Figure 5
(B) shows the second wavelength and T after the first wavelength is selected.
It is a figure which shows the multiple correlation coefficient with a BARS value. From this figure, in the wavelength region where the multiple correlation coefficient is stable with respect to fluctuations of each wavelength, a wavelength having a stable coefficient (K2) was searched for, and 978 nm was selected as the second wavelength. FIG. 5C shows the TBA and the third wavelength after selecting the first and second wavelengths.
It is a figure which shows the multiple correlation coefficient with RS value. From this figure, in the wavelength region where the multiple correlation coefficient is stable with respect to fluctuations of each wavelength, a wavelength having a stable coefficient (K3) was searched for, and 722 nm was selected as the third wavelength. From the figure showing the multiple correlation coefficient between the TBARS value and the fourth wavelength after selecting the first, second and third wavelengths, the wavelength at which the multiple correlation coefficient is stable with respect to variations in each wavelength In the region, a wavelength having a stable coefficient (K4) was searched for, and 496 nm was selected as the fourth wavelength.

Four types of calibration curve models (calibration curve using the first wavelength,
Calibration curves using first and second wavelengths, first, second and third
For a calibration curve using wavelengths, a calibration curve using the first, second, third and fourth wavelengths), the accuracy evaluation is performed by the cross-validation method, and cross-validation is performed with an increase in the multiple correlation coefficient of the calibration curve model. The most accurate calibration curve with the fewest explanatory variables (number of wavelengths) was examined by comparing with the reduction of the standard error of prediction by the method. The results are shown in Table 3. As a result, a calibration curve using the above first, second and third wavelengths was selected.

[0039]

[Table 3]

In FIG. 6, the vertical axis represents the measured value by the method of this embodiment, and the horizontal axis represents the analytical value by the conventional method, in which the measuring points for each of the 48 samples are plotted. as a result,
The multiple correlation coefficient (R) between both measured values was 0.891, and the standard error (SEC) was 0.325 mg MDA / kg, which showed a high correlation.

Here, for example, the first wavelength is 618n
m, the second wavelength is 976 nm, and the third wavelength is 720
When nm is selected, the multiple correlation coefficient of this calibration curve is 0.88.
4, the standard error is 0.334mg MDA / kg meat,
622 nm as the first wavelength and 980 n as the second wavelength
m and 724 nm is selected as the third wavelength, the multiple correlation coefficient of this calibration curve is 0.894 and the standard error is 0.321 m.
It becomes gMDA / kg meat. As described above, even if the first wavelength, the second wavelength, and the third wavelength are changed in the range of ± 2 nm, almost the same high correlation can be obtained, and the coefficient is K0.
= 2.40 to 2.90, K1 = -9.78 to -8.9.
9, K2 = -17.77 to -16.42, K3 = -5
If a value within the range of 3.52 to −30.78 is used, a multiple correlation coefficient of 0.884 to 0.894 and a standard error of 0.321 to
A sufficiently high correlation can be obtained with 0.334 mg MDA / kg meat.

The degree of lipid oxidation of the beef thus obtained (TB
Using the calibration curve of (ARS), it becomes possible to accurately measure the lipid oxidation degree of beef in a short period of time at the distribution and retail stages. Since an increase in TBARS value indicates a decrease in freshness, the TBARS value measured by the method of the present invention is 0.6 m.
If gMDA / kg meat or more, it means that the freshness of beef has deteriorated. TBARS value is 0.6 mg MDA / kg meat.
It can be determined that the freshness is good if it is below.

[Embodiment 2] Pork (1) Redness (a *) calibration curve Three kinds of wavelengths in the redness (a *) calibration curve of pork are selected as follows. FIG. 7A shows a
It is a figure which shows the single correlation coefficient of each wavelength with respect to *. From this figure, find the wavelength where the coefficient (K1) is stable in the wavelength region where the single correlation coefficient is stable with respect to the fluctuation of each wavelength,
588 nm was selected as the first wavelength. FIG.7 (b) is
It is a figure which shows the multiple correlation coefficient of the 2nd wavelength and a * after selecting the 1st wavelength. From this figure, in the wavelength region where the multiple correlation coefficient is stable with respect to fluctuations of each wavelength, the coefficient (K
2) also searches for a stable wavelength, and sets 688 as the second wavelength.
nm was selected (610-700 nm is the red wavelength range). FIG.7 (c) is a figure which shows the multiple correlation coefficient of the 3rd wavelength and a * after selecting the 1st and 2nd wavelength. From this figure, in the wavelength region where the multiple correlation coefficient is stable with respect to fluctuations of each wavelength, a wavelength having a stable coefficient (K3) was searched for, and 556 nm was selected as the third wavelength. From the figure showing the multiple correlation coefficient of the fourth wavelength and a * after selecting the first, second and third wavelengths, the wavelength at which the multiple correlation coefficient is stable with respect to fluctuations of each wavelength In the area, coefficient (K4)
Also looking for a stable wavelength, 950 nm as the fourth wavelength
Was selected. 4 types of calibration curve models (calibration curves using the first wavelength,
Calibration curves using first and second wavelengths, first, second and third
For a calibration curve using wavelengths, a calibration curve using the first, second, third and fourth wavelengths), the accuracy evaluation is performed by the cross-validation method, and cross-validation is performed with an increase in the multiple correlation coefficient of the calibration curve model. The most accurate calibration curve with the fewest explanatory variables (number of wavelengths) was examined by comparing with the reduction of the standard error of prediction by the method. The results are shown in Table 4. As a result, a calibration curve using the above first, second and third wavelengths was selected.

[0044]

[Table 4]

In FIG. 8, the vertical axis represents the measured value by the method of the present embodiment, and the horizontal axis represents the analytical value by the conventional method, in which 96 measuring points for each sample are plotted. as a result,
The multiple correlation coefficient (R) between both measured values was 0.948, and the standard error (SEC) was 0.75, showing a very high correlation.

Here, for example, the first wavelength is 586n.
m, the second wavelength is 686 nm, the third wavelength is 554
When nm is selected, the multiple correlation coefficient of this calibration curve is 0.94.
8. The standard error is 0.76, and the first wavelength is 590.
nm, the second wavelength is 690 nm, and the third wavelength is 55
When 8 nm is selected, the multiple correlation coefficient of this calibration curve is 0.9
34, and the standard error is 0.85. In this way, even if the first wavelength, the second wavelength, and the third wavelength are changed in the range of ± 2 nm, almost the same high correlation can be obtained, and the coefficient K0 = -1.77 to -0.44. , K1 = −35.
67 to -26.95, K2 = 40.51 to 46.07,
If a value within the range of K3 = -16.59 to -15.57 is used, a sufficiently high correlation with a multiple correlation coefficient of 0.934 to 0.948 and a standard error of 0.75 to 0.85 can be obtained. it can.

By using the calibration curve of the redness (a *) of pork thus obtained, the change in the redness of pork can be measured non-destructively and accurately in a short time in the distribution and retail stages. A decrease in redness indicates a decrease in freshness. Therefore, if the redness measured by the method of the present invention is 6 or less, it means that the pork is not fresh, and if the redness is 6 or more, the freshness is good. Can be determined.

(2) Calibration curve for the rate of metmyoglobin formation The three wavelengths in the calibration curve for the rate of metmyoglobin formation of pork are selected as follows. Figure 9
(A) is a figure showing the single correlation coefficient of each wavelength with respect to the metmyoglobin formation rate. From this figure, in the wavelength region where the single correlation coefficient is stable with respect to the fluctuation of each wavelength, the wavelength where the coefficient (K1) is also stable is searched for, and 616 nm is set as the first wavelength.
Selected as wavelength. FIG. 9B is a diagram showing a multiple correlation coefficient between the second wavelength after selecting the first wavelength and the metmyoglobin formation rate. From this figure, in the wavelength region where the multiple correlation coefficient is stable with respect to the fluctuation of each wavelength, the wavelength where the coefficient (K2) is also stable is searched for, and the second wavelength is 6
94 nm was selected. FIG. 9C is a diagram showing a multiple correlation coefficient between the third wavelength and the metmyoglobin formation rate after selecting the first and second wavelengths. From this figure, a wavelength in which the coefficient (K3) is stable is searched for in the wavelength region in which the multiple correlation coefficient is stable with respect to fluctuations in each wavelength, and 744 nm is selected as the third wavelength. From the figure showing the multiple correlation coefficient between the fourth wavelength and the metmyoglobin formation rate after selecting the first, second and third wavelengths, it can be seen that the multiple correlation coefficient is stable with respect to variations in each wavelength. Coefficient (K4)
Also find a stable wavelength, 658nm as the fourth wavelength
Was selected. 4 types of calibration curve models (calibration curves using the first wavelength,
Calibration curves using first and second wavelengths, first, second and third
For a calibration curve using wavelengths, a calibration curve using the first, second, third and fourth wavelengths), the accuracy evaluation is performed by the cross-validation method, and cross-validation is performed with an increase in the multiple correlation coefficient of the calibration curve model. The most accurate calibration curve with the fewest explanatory variables (number of wavelengths) was examined by comparing with the reduction of the standard error of prediction by the method. The results are shown in Table 5. As a result, a calibration curve using the above first, second and third wavelengths was selected.

[0049]

[Table 5]

In FIG. 10, the vertical axis represents the measured value by the method of this embodiment, and the horizontal axis represents the analytical value by the conventional method, in which 96 measurement points for each sample are plotted. As a result, the multiple correlation coefficient (R) between both measured values was 0.965,
The standard error (SEC) was 4.50%, which was a very high correlation.

Here, for example, the first wavelength is 614n
m, the second wavelength is 692 nm, the third wavelength is 742
When nm is selected, the multiple correlation coefficient of this calibration curve is 0.96.
5. The standard error is 4.51%, which is 61 as the first wavelength.
8 nm, second wavelength is 696 nm, third wavelength is 7
When 46 nm is selected, the multiple correlation coefficient of this calibration curve is 0.
956, the standard error is 5.03%. Like this first
Even if the wavelength, the second wavelength, and the third wavelength are changed in a range of ± 2 nm, almost the same high correlation can be obtained, and the coefficients are K0 = 51.28 to 61.10 and K1 = -20.
4.22--191.43, K2 = 381.67-44
If a value within the range of 5.55, K3 = 613.28 to 1901.96 is used, the multiple correlation coefficient is 0.956 to 0.96.
5, a sufficiently high correlation with a standard error of 4.50 to 5.03% can be obtained.

By using the calibration curve of the rate of metmyoglobin formation in pork thus obtained, the rate of metmyoglobin formation in pork can be measured non-destructively and accurately in a distribution or retail stage. Since an increase in the metmyoglobin formation rate indicates a decrease in freshness, if the metmyoglobin formation rate measured by the method of the present invention is 60% or more, it means that the pork freshness is low, and the metmyoglobin formation rate is 60% or less. If so, it can be determined that the freshness is good.

(3) Calibration curve of lipid oxidation degree (TBARS) The four kinds of wavelengths in the calibration curve of lipid oxidation degree (TBARS) of pork are selected as follows. Figure 11
(A) is a figure showing the single correlation coefficient of each wavelength with respect to a TBARS value. From this figure, a wavelength in which the coefficient (K1) is stable is searched for in a wavelength region in which the single correlation coefficient is stable with respect to fluctuations in each wavelength, and 604 nm is selected as the first wavelength. FIG. 11B is a diagram showing a multiple correlation coefficient between the second wavelength after selecting the first wavelength and the TBARS value. From this figure, in the wavelength region where the multiple correlation coefficient is stable with respect to fluctuations of each wavelength, a wavelength having a stable coefficient (K2) was searched for, and 934 nm was selected as the second wavelength. Figure 1
FIG. 1 (c) is a diagram showing a multiple correlation coefficient between the TBARS value and the third wavelength after selecting the first and second wavelengths.
From this figure, a wavelength in which the coefficient (K3) is stable is searched for in the wavelength region in which the multiple correlation coefficient is stable with respect to changes in each wavelength, and 480 nm is selected as the third wavelength. Figure 11
(D) shows the fourth wavelength after selecting the first, second and third wavelengths.
It is a figure which shows the multiple correlation coefficient of the th wavelength and TBARS value. From this figure, a wavelength in which the coefficient (K4) is stable is searched for in the wavelength region in which the multiple correlation coefficient is stable with respect to fluctuations in each wavelength, and 748 nm is selected as the fourth wavelength.

The above-mentioned four calibration curve models (calibration curve using the first wavelength,
Calibration curves using first and second wavelengths, first, second and third
For a calibration curve using wavelengths, a calibration curve using the first, second, third and fourth wavelengths), the accuracy evaluation is performed by the cross-validation method, and cross-validation is performed with an increase in the multiple correlation coefficient of the calibration curve model. The most accurate calibration curve with the fewest explanatory variables (number of wavelengths) was examined by comparing with the reduction of the standard error of prediction by the method. The results are shown in Table 6. As a result, the above first, second,
A calibration curve using the third and fourth wavelengths was selected.

[0055]

[Table 6]

In FIG. 12, the vertical axis represents the measured value by the method of this embodiment, and the horizontal axis represents the analytical value by the conventional method, in which the measuring points for each of the 96 samples are plotted. As a result, the multiple correlation coefficient (R) between both measured values was 0.840,
The standard error (SEC) was highly correlated with 0.106 mg MDA / kg meat.

Here, for example, the first wavelength is 602n
m, the second wavelength is 932 nm, and the third wavelength is 478.
nm and 746 nm as the fourth wavelength, the multiple correlation coefficient of this calibration curve is 0.821 and the standard error is 0.112.
mg MDA / kg meat, 606n as the first wavelength
m, the second wavelength is 936 nm, and the third wavelength is 482.
nm, and when 750 nm is selected as the fourth wavelength, the multiple correlation coefficient of this calibration curve is 0.817 and the standard error is 0.113.
It becomes mg MDA / kg meat. In this way, the first wavelength, the second
Even if the wavelength, the third wavelength, and the fourth wavelength are changed within a range of ± 2 nm, almost the same high correlation can be obtained, and the coefficients are K0 = 1.05 to 1.21 and K1 = −2.53.
~ -2.31, K2 = -6.41 to -3.95, K3 =
-0.42 to -0.11, K4 = 10.04 to 19.8
If a value within the range of 5 is used, the multiple correlation coefficient is 0.817-
0.840, standard error 0.106 to 0.113 mg MD
A sufficiently high correlation with A / kg meat can be obtained.

The degree of lipid oxidation of the pork thus obtained (TB
Using the calibration curve of (ARS), it becomes possible to accurately measure the lipid oxidation degree of pork in a short time in a distribution or retail stage. Since an increase in TBARS value indicates a decrease in freshness, the TBARS value measured by the method of the present invention is 0.6 m.
If gMDA / kg meat or more, pork is not fresh, TBARS value is 0.6 mg MDA / kg meat
It can be determined that the freshness is good if it is below.

[Embodiment 3] Chicken (1) Standard curve of redness (a *) The three kinds of wavelengths in the calibration curve of redness (a *) of chicken are selected as follows. FIG. 13A shows
It is a figure which shows the single correlation coefficient of each wavelength with respect to a *. From this figure, a wavelength in which the coefficient (K1) is stable is searched for in a wavelength region in which the single correlation coefficient is stable with respect to fluctuations in each wavelength, and 576 nm is selected as the first wavelength. FIG.
(B) is the second wavelength after selecting the first wavelength and a
It is a figure which shows the multiple correlation coefficient with *. From this figure, in the wavelength region where the multiple correlation coefficient is stable with respect to fluctuations of each wavelength
A wavelength having a stable coefficient (K2) was searched for, and 462 nm was selected as the second wavelength. FIG. 13C is a diagram showing a multiple correlation coefficient between the third wavelength and a * after selecting the first and second wavelengths. From this figure, coefficient (K3)
Also looking for a stable wavelength, 1018n as the third wavelength
m was selected. From the figure showing the multiple correlation coefficient of the fourth wavelength and a * after selecting the first, second and third wavelengths, the wavelength at which the multiple correlation coefficient is stable with respect to fluctuations of each wavelength In the region, a wavelength having a stable coefficient (K4) was searched for, and 738 nm was selected as the fourth wavelength. The four types of calibration curve models that sequentially incorporate the above four types of wavelengths as explanatory variables (the calibration line using the first wavelength, the calibration line using the first and second wavelengths, the first, second and third wavelengths For the calibration curve used, the calibration curve using the first, second, third, and fourth wavelengths), the accuracy is evaluated by the cross-validation method, and the increase of the multiple correlation coefficient of the calibration curve model and the cross-validation method are used. The most accurate calibration curve with the least explanatory variables (number of wavelengths) was examined by comparing with the decrease of the prediction standard error. The results are shown in Table 7. As a result, a calibration curve using the above first, second and third wavelengths was selected.

[0060]

[Table 7]

In FIG. 14, the vertical axis represents the measured value by the method of this embodiment and the horizontal axis represents the analytical value by the conventional method, and the 24 measured points for each sample are plotted. As a result, the multiple correlation coefficient (R) between both measured values was 0.869,
The standard error (SEC) was 0.43, indicating a high correlation.

Here, for example, the first wavelength is 574n.
m, the second wavelength is 460 nm, the third wavelength is 101
When 6 nm is selected, the multiple correlation coefficient of this calibration curve is 0.8
44, the standard error is 0.46, and the first wavelength is 57.
8 nm, second wavelength is 464 nm, third wavelength is 1
When 020 nm is selected, the multiple correlation coefficient of this calibration curve is 0.869 and the standard error is 0.43. As described above, even if the first wavelength, the second wavelength, and the third wavelength are changed in the range of ± 2 nm, substantially the same high correlation can be obtained, and the coefficients are K0 = 3.87 to 4.73 and K1. = -31.
48 to -18.47, K2 = -14.70 to -12.4
3, K3 = 167.13 to 395.84 is used, it is possible to obtain a sufficiently high correlation with a multiple correlation coefficient of 0.844 to 0.869 and a standard error of 0.43 to 0.46. it can.

By using the calibration curve of the redness (a *) of chicken thus obtained, it becomes possible to accurately and nondestructively measure the change in redness of chicken in a distribution or retail stage in a short time. Since a decrease in redness indicates a decrease in freshness, if the redness measured by the method of the present invention is 3 or less, the freshness of chicken is low, and if the redness is 3 or more, the freshness is good. Can be determined.

(2) Calibration curve of metmyoglobin formation rate The three wavelengths in the calibration curve of metmyoglobin formation rate of chicken are selected as follows. Figure 15
(A) is a figure showing the single correlation coefficient of each wavelength with respect to the metmyoglobin formation rate. From this figure, in the wavelength region in which the single correlation coefficient is stable with respect to fluctuations in each wavelength, the wavelength in which the coefficient (K1) is also stable is searched for, and 618 nm is set as the first wavelength.
The wavelength was selected (the wavelength in the same region as the first wavelength of the metmyoglobin formation rate of pork was selected). Figure 15
(B) is a figure which shows the multiple correlation coefficient of the 2nd wavelength after selecting the 1st wavelength, and the metmyoglobin formation rate. From this figure, in the wavelength region where the multiple correlation coefficient is stable with respect to fluctuations of each wavelength, a wavelength having a stable coefficient (K2) was searched for, and 998 nm was selected as the second wavelength. Figure 1
FIG. 5C is a diagram showing a multiple correlation coefficient between the third wavelength after selecting the first and second wavelengths and the metmyoglobin formation rate. From this figure, in the wavelength region where the multiple correlation coefficient is stable with respect to fluctuations of each wavelength, a wavelength having a stable coefficient (K3) was searched for, and 832 nm was selected as the third wavelength. From the figure showing the multiple correlation coefficient between the fourth wavelength and the metmyoglobin formation rate after selecting the first, second and third wavelengths, it can be seen that the multiple correlation coefficient is stable with respect to variations in each wavelength. In the existing wavelength region, a wavelength having a stable coefficient (K4) was searched for, and 538 nm was selected as the fourth wavelength. The four types of calibration curve models that sequentially incorporate the above four types of wavelengths as explanatory variables (the calibration line using the first wavelength, the calibration line using the first and second wavelengths, the first, second and third wavelengths For the calibration curve used, the calibration curve using the first, second, third, and fourth wavelengths), the accuracy is evaluated by the cross-validation method, and the increase of the multiple correlation coefficient of the calibration curve model and the cross-validation method are used. The most accurate calibration curve with the least explanatory variables (number of wavelengths) was examined by comparing with the decrease of the prediction standard error. The results are shown in Table 8. As a result, a calibration curve using the above first, second and third wavelengths was selected.

[0065]

[Table 8]

In FIG. 16, the ordinate represents the measured value by the method of this embodiment and the abscissa represents the analytical value by the conventional method, and the measured points for each of the 24 samples are plotted. As a result, the multiple correlation coefficient (R) between both measured values was 0.972,
The standard error (SEC) was 2.44%, which is a very high correlation.

Here, for example, the first wavelength is 616n
m, the second wavelength is 996 nm, the third wavelength is 830
When nm is selected, the multiple correlation coefficient of this calibration curve is 0.97.
1, the standard error is 2.49%, 62 as the first wavelength
When 0 nm, the second wavelength is 1000 nm, and the third wavelength is 834 nm, the multiple correlation coefficient of this calibration curve is 0.954 and the standard error is 3.12%. In this way, even if the first wavelength, the second wavelength, and the third wavelength are changed in the range of ± 2 nm, almost the same high correlation can be obtained, and the coefficients K0 = 30.31 to 38.76, K1. =-
343.08 to -278.65, K2 = -1065.7.
3 to -895.98, K3 = -5067.40 to -37.
If a value within the range of 53.50 is used, the multiple correlation coefficient is 0.9.
54 to 0.972, standard error 2.44 to 3.12%,
A sufficiently high correlation can be obtained.

By using the calibration curve of the rate of metmyoglobin formation of chicken thus obtained, the rate of metmyoglobin formation of chicken can be accurately measured in a short time in a non-destructive manner at the distribution or retail stage. Since an increase in the metmyoglobin formation rate indicates a decrease in freshness, if the metmyoglobin formation rate measured by the method of the present invention is 50% or more, it means that the chicken's freshness is low, and the metmyoglobin formation rate is 50% or less. If so, it can be determined that the freshness is good.

(3) Calibration curve of lipid oxidation degree (TBARS) Three kinds of wavelengths in the calibration curve of lipid oxidation degree (TBARS) of chicken are selected as follows. FIG. 17
(A) is a figure showing the single correlation coefficient of each wavelength with respect to a TBARS value. From this figure, a wavelength in which the coefficient (K1) is stable is searched for in a wavelength region in which the single correlation coefficient is stable with respect to changes in each wavelength, and 626 nm is selected as the first wavelength. FIG.17 (b) is a figure which shows the multiple correlation coefficient of the 2nd wavelength after selecting a 1st wavelength, and a TBARS value. From this figure, a wavelength in which the coefficient (K2) is stable is searched for in the wavelength region in which the multiple correlation coefficient is stable with respect to variations in each wavelength, and 552 nm is selected as the second wavelength. Figure 1
FIG. 7C is a diagram showing a multiple correlation coefficient between the TBARS value and the third wavelength after selecting the first and second wavelengths.
From this figure, in the wavelength region where the multiple correlation coefficient is stable with respect to fluctuations of each wavelength, a wavelength having a stable coefficient (K3) was searched for, and 832 nm was selected as the third wavelength. TBA with 4th wavelength after selecting 1st, 2nd and 3rd wavelength
From the figure showing the multiple correlation coefficient with the RS value, the coefficient (K
4) also searches for a stable wavelength, and sets 670 as the fourth wavelength.
nm was selected. The four types of calibration curve models that sequentially incorporate the above four types of wavelengths as explanatory variables (the calibration line using the first wavelength, the calibration line using the first and second wavelengths, the first, second and third wavelengths For the calibration curve used, the calibration curve using the first, second, third, and fourth wavelengths), the accuracy is evaluated by the cross-validation method, and the increase of the multiple correlation coefficient of the calibration curve model and the cross-validation method are used. The most accurate calibration curve with the least explanatory variables (number of wavelengths) was examined by comparing with the decrease of the prediction standard error. The results are shown in Table 9. As a result, a calibration curve using the above first, second and third wavelengths was selected.

[0070]

[Table 9]

In FIG. 18, the vertical axis represents the measured value by the method of this embodiment and the horizontal axis represents the analytical value by the ordinary method, and the measurement points for each of the 24 samples are plotted. As a result, the multiple correlation coefficient (R) between both measured values was 0.669,
Standard error (SEC) showed a slightly lower correlation with 0.330 mg MDA / kg meat.

Here, for example, the first wavelength is 624n.
m, the second wavelength is 550 nm, the third wavelength is 830
When nm is selected, the multiple correlation coefficient of this calibration curve is 0.69.
6. The standard error is 0.319mg MDA / kg meat,
628 nm as the first wavelength and 554n as the second wavelength
m and 834 nm is selected as the third wavelength, the multiple correlation coefficient of this calibration curve is 0.615 and the standard error is 0.350 m.
It becomes gMDA / kg meat. As described above, even if the first wavelength, the second wavelength, and the third wavelength are changed in the range of ± 2 nm, almost the same high correlation can be obtained, and the coefficient is K0.
= -1.43 to 0.29, K1 = -22.34 to -1
7.11, K2 = -15.55 to -9.53, K3 = 3
If a value within the range of 92.02 to 456.21 is used, the multiple correlation coefficient is 0.615 to 0.696 and the standard error is 0.319.
A sufficiently high correlation can be obtained with ~ 0.350 mg MDA / kg meat.

The degree of lipid oxidation of the chicken thus obtained (TB
When the calibration curve of ARS) is used, the lipid oxidation degree of chicken can be measured non-destructively and in a short time in the distribution and retail stages. Since an increase in TBARS value indicates a decrease in freshness, the TBARS value measured by the method of the present invention is 0.6 m.
If gMDA / kg meat or more, it means that the freshness of chicken has deteriorated. TBARS value is 0.6 mg MDA / kg meat.
It can be determined that the freshness is good if it is below.

[0074]

EFFECTS OF THE INVENTION Regarding the measurement values obtained by the calibration curve of this embodiment, for example, beef has a redness (a *) of 16
Below, metmyoglobin formation rate is more than 30%, TBA
If the RS value is 0.6 mg MDA / kg meat or more, it can be determined that the freshness of the beef has deteriorated, the redness (a *) is 16 or more, and the metmyoglobin formation rate is 30%. Below, TBARS value is 0.6 mg
If MDA / kg meat or less, it can be judged that the freshness of beef is good. By using the present invention, the redness, discoloration rate, and lipid oxidation degree of meat can be accurately determined in a short time (about 10 seconds) in a nondestructive manner, which is useful for objective evaluation of freshness of meat during distribution and sale. .

[Brief description of drawings]

1A is a diagram showing a single correlation coefficient of a first wavelength with respect to redness (a *) of beef, FIG. 1B is a diagram showing a multiple correlation coefficient of a second wavelength, and FIG. The figure showing the multiple correlation coefficient of 3 wavelengths.

FIG. 2 is a diagram showing the relationship between the analysis value of redness (a *) of beef by the method of the present invention and the analysis value by a conventional method.

FIG. 3A is a diagram showing a single correlation coefficient at a first wavelength with respect to the beet's metmyoglobin formation ratio, and FIG. 3B is a diagram showing a multiple correlation coefficient at a second wavelength.

FIG. 4 is a diagram showing the relationship between the analysis value of the metmyoglobin formation rate of beef by the method of the present invention and the analysis value by the conventional method.

5A is a diagram showing a single correlation coefficient at a first wavelength with respect to beef lipid oxidation degree (TBARS), FIG. 5B is a diagram showing a multiple correlation coefficient at a second wavelength, and FIG. The figure showing the multiple correlation coefficient of 3 wavelengths.

FIG. 6: Lipid oxidation degree (TBA) of beef according to the method of the present invention.
FIG. 4 is a diagram showing the relationship between the analysis value of (RS) and the analysis value obtained by a conventional method.

7A is a diagram showing a single correlation coefficient at a first wavelength with respect to redness (a *) of pork, FIG. 7B is a diagram showing a multiple correlation coefficient at a second wavelength, and FIG. The figure showing the multiple correlation coefficient of wavelength.

FIG. 8 is a diagram showing the relationship between the analysis value of pork redness (a *) according to the method of the present invention and the analysis value according to a conventional method.

9A is a diagram showing a single correlation coefficient at a first wavelength with respect to a metmyoglobin formation rate of pork, FIG. 9B is a diagram showing a multiple correlation coefficient at a second wavelength, and FIG. 9C is a third wavelength. The figure showing the multiple correlation coefficient of.

FIG. 10 is a diagram showing the relation between the analysis value of the metmyoglobin formation rate of pork by the method of the present invention and the analysis value by the conventional method.

11A is a diagram showing a single correlation coefficient at a first wavelength with respect to a lipid oxidation degree (TBARS) of pork, FIG. 11B is a diagram showing a multiple correlation coefficient at a second wavelength, and FIG. The figure showing the multiple correlation coefficient of 3 wavelengths, (d) is a figure showing the multiple correlation coefficient of a 4th wavelength.

FIG. 12: Degree of lipid oxidation of pork (TB) by the method of the present invention
FIG. 4 is a diagram showing the relationship between the analysis value of ARS) and the analysis value by the conventional method.

FIG. 13 (a) is the first with respect to the redness (a *) of chicken.
The figure showing the single correlation coefficient of a wavelength, (b) the figure showing the multiple correlation coefficient of the 2nd wavelength, (c) the figure showing the multiple correlation coefficient of the 3rd wavelength.

FIG. 14 is a diagram showing the relationship between the analysis value of redness (a *) of chicken meat by the method of the present invention and the analysis value by the conventional method.

15A is a diagram showing a single correlation coefficient at a first wavelength with respect to a metmyoglobin formation rate of chicken, FIG. 15B is a diagram showing a multiple correlation coefficient at a second wavelength, and FIG. 15C is a third wavelength. The figure showing the multiple correlation coefficient of.

FIG. 16 is a diagram showing the relationship between the analysis value of the metmyoglobin formation rate of chicken meat by the method of the present invention and the analysis value by the conventional method.

FIG. 17 (a) is a diagram showing a single correlation coefficient of a first wavelength with respect to a lipid oxidation degree (TBARS) of chicken, (b) is a diagram showing a multiple correlation coefficient of a second wavelength, and (c) is a diagram. The figure showing the multiple correlation coefficient of 3 wavelengths.

FIG. 18: Degree of lipid oxidation (TB) of chicken according to the method of the present invention
FIG. 4 is a diagram showing the relationship between the analysis value of ARS) and the analysis value by the conventional method.

   ─────────────────────────────────────────────────── ─── Continued front page    (72) Inventor Hitoshi Murakami             1-17, Azuma, Tsukuba, Ibaraki 403             Building 1212 F term (reference) 2G059 AA03 BB11 EE01 EE02 EE12                       EE13 HH01 HH02 HH06 MM01                       MM02 MM12

Claims (9)

[Claims] 1. The transmission reflectance of beef during storage is measured, and the second derivative X1 of the log (1 / transmission reflectance) at a wavelength of 620 ± 2 nm and the log (1 / transmission reflectance) at a wavelength of 744 ± 2 nm. 2nd derivative value of X2 and 2nd derivative value of log (1 / transmissivity) X3 at wavelength of 590 ± 2nm are calculated, and freshness of beef in storage is judged based on redness Y obtained by the following calibration curve. A method for determining freshness of meat, characterized by: Y = K0 + K1X1 + K2X2 + K3X3 (K0 = 2.55 to 7.47, K1 = 77.30 to 9)
8.96, K2 = 303.10-513.63, K3 =
42.72 to 52.93) 2. The transmission reflectance of beef during storage is measured, and the second derivative X1 of the log (1 / transmission reflectance) at a wavelength of 624 ± 2 nm and the log (1 / transmission reflectance) at a wavelength of 1020 ± 2 nm. The method for determining the freshness of meat is characterized in that the freshness of beef under storage is determined based on the metmyoglobin formation rate Y obtained by the following calibration curve. Y = K0 + K1X1 + K2X2 (K0 = 42.24 to 77.33, K1 = -225.7)
9-218.18, K2 = 676.881-1673.
07) 3. The transmission reflectance of beef during storage is measured, and the second derivative X1 of the log (1 / transmission reflectance) at a wavelength of 620 ± 2 nm and the log (1 / transmission reflectance) at a wavelength of 978 ± 2 nm. Second-order derivative value X2, log (1 / transmissivity / reflectance) second-order derivative value X3 at a wavelength of 722 ± 2 nm, and based on the lipid oxidation degree (TBARS) Y obtained from the following calibration curve, beef stored. A method for determining freshness of meat, characterized by determining freshness of meat. Y = K0 + K1X1 + K2X2 + K3X3 (K0 = 2.40 to 2.90, K1 = −9.78 to −
8.99, K2 = -17.77 to -16.42, K3 =
-53.52--30.78) 4. The transmission reflectance of pork during storage is measured, and the second derivative X1 of the log (1 / transmission reflectance) at a wavelength of 588 ± 2 nm and the log (1 / transmission reflectance) at a wavelength of 688 ± 2 nm. 2nd derivative value of X2 and 2nd derivative value of log (1 / transmissivity / reflectance) at wavelength 556 ± 2nm are calculated, and the freshness of the stored pork is judged based on the redness Y obtained by the following calibration curve. A method for determining freshness of meat, characterized by: Y = K0 + K1X1 + K2X2 + K3X3 (K0 = -1.77 to -0.44, K1 = -35.67
~ -26.95, K2 = 40.51 to 46.07, K3
= -16.59 to -15.57) 5. The transmission reflectance of the stored pork is measured, and the second derivative X1 of the log (1 / transmission reflectance) at a wavelength of 616 ± 2 nm and the log (1 / transmission reflectance) at a wavelength of 694 ± 2 nm. The second-order derivative value X2 and the second-order derivative value X3 of the log (1 / transmissivity / reflectance) at a wavelength of 744 ± 2 nm are calculated, and the freshness of the stored pork is stored based on the metmyoglobin formation rate Y obtained by the following calibration curve. A method for determining freshness of meat, characterized by determining. Y = K0 + K1X1 + K2X2 + K3X3 (K0 = 51.28 to 61.10, K1 = −204.2)
2--191.43, K2 = 381.67-445.5.
5, K3 = 613.28 to 1901.96) 6. The transmission reflectance of pork during storage is measured to obtain a second derivative X1 of log (1 / transmission reflectance) at a wavelength of 604 ± 2 nm and log (1 / transmission reflectance) at a wavelength of 934 ± 2 nm. Second derivative value X2, second derivative value X3 of log (1 / transmissivity) at wavelength 480 ± 2 nm, wavelength 748
Second derivative of log (1 / transmissivity) at ± 2 nm
4 is obtained, and the lipid oxidation degree (T
A method for determining freshness of meat, comprising determining the freshness of stored pork based on BARS) Y. Y = K0 + K1X1 + K2X2 + K3X3 + K4X4 (K0 = 1.05 to 1.21, K1 = −2.53 to −
2.31, K2 = -6.41 to -3.95, K3 =-
0.42 to -0.11, K4 = 10.04 to 19.8
5) 7. The transmission reflectance of chicken meat during storage is measured, and the second derivative X1 of the log (1 / transmission reflectance) at a wavelength of 576 ± 2 nm and the log (1 / transmission reflectance) at a wavelength of 462 ± 2 nm. 2nd derivative value X2, and second derivative value X3 of log (1 / transmissivity / reflectance) at a wavelength of 1018 ± 2nm are determined, and the freshness of the stored chicken is determined based on the redness Y obtained by the following calibration curve. A method for determining freshness of meat, characterized by: Y = K0 + K1X1 + K2X2 + K3X3 (K0 = 3.87 to 4.73, K1 = −31.48 to −
18.47, K2 = -14.70 to -12.43, K3
= 167.13 to 395.84) 8. The transmission reflectance of chicken meat during storage is measured, and the second derivative X1 of the log (1 / transmission reflectance) at a wavelength of 618 ± 2 nm and the log (1 / transmission reflectance) at a wavelength of 998 ± 2 nm. The second-order derivative value X2 and the second-order derivative value X3 of the log (1 / transmissivity / reflectance) at a wavelength of 832 ± 2 nm are calculated, and the freshness of the chicken meat is stored based on the metmyoglobin formation rate Y obtained by the following calibration curve. A method for determining freshness of meat, characterized by determining. Y = K0 + K1X1 + K2X2 + K3X3 (K0 = 30.31 to 38.76, K1 = -343.0
8 to -278.65, K2 = -1065.73 to -89.
5.98, K3 = -5067.40 to -3753.5
0) 9. The transmission reflectance of chicken meat during storage is measured, and the second derivative X1 of the log (1 / transmission reflectance) at a wavelength of 626 ± 2 nm and the log (1 / transmission reflectance) at a wavelength of 552 ± 2 nm. The second-order derivative value X2 and the second-order derivative value X3 of the log (1 / transmissivity / reflectance) at a wavelength of 832 ± 2 nm are obtained, and the chicken meat is stored based on the lipid oxidation degree (TBARS) Y obtained by the following calibration curve. A method for determining freshness of meat, characterized by determining freshness of meat. Y = K0 + K1X1 + K2X2 + K3X3 (K0 = −1.43 to 0.29, K1 = −22.34 to
-17.11, K2 = -15.55 to -9.53, K3
= 392.02 to 456.21)