KR20140078597A - Method for measuring pH, water content and lactic acid content of Italian ryegrass silage using near infrared spectroscopy - Google Patents
Method for measuring pH, water content and lactic acid content of Italian ryegrass silage using near infrared spectroscopy Download PDFInfo
- Publication number
- KR20140078597A KR20140078597A KR1020140067072A KR20140067072A KR20140078597A KR 20140078597 A KR20140078597 A KR 20140078597A KR 1020140067072 A KR1020140067072 A KR 1020140067072A KR 20140067072 A KR20140067072 A KR 20140067072A KR 20140078597 A KR20140078597 A KR 20140078597A
- Authority
- KR
- South Korea
- Prior art keywords
- infrared spectrum
- calibration equation
- infrared
- sample
- silage
- Prior art date
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- JVTAAEKCZFNVCJ-UHFFFAOYSA-N lactic acid Chemical compound CC(O)C(O)=O JVTAAEKCZFNVCJ-UHFFFAOYSA-N 0.000 title claims abstract description 48
- 239000004460 silage Substances 0.000 title claims abstract description 39
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 33
- 235000014655 lactic acid Nutrition 0.000 title claims abstract description 24
- 239000004310 lactic acid Substances 0.000 title claims abstract description 24
- 238000004497 NIR spectroscopy Methods 0.000 title abstract description 14
- 240000004296 Lolium perenne Species 0.000 title abstract description 11
- 238000002329 infrared spectrum Methods 0.000 claims abstract description 40
- 241000209094 Oryza Species 0.000 claims description 18
- 235000007164 Oryza sativa Nutrition 0.000 claims description 18
- 235000009566 rice Nutrition 0.000 claims description 18
- 238000001228 spectrum Methods 0.000 claims description 16
- 238000007781 pre-processing Methods 0.000 claims description 12
- 230000004069 differentiation Effects 0.000 claims description 11
- 238000004611 spectroscopical analysis Methods 0.000 claims description 4
- 230000005540 biological transmission Effects 0.000 claims description 2
- 239000002893 slag Substances 0.000 claims 1
- 230000001939 inductive effect Effects 0.000 abstract 1
- 238000004458 analytical method Methods 0.000 description 28
- 239000000523 sample Substances 0.000 description 17
- 230000001066 destructive effect Effects 0.000 description 8
- 238000005259 measurement Methods 0.000 description 5
- 238000012795 verification Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 238000000611 regression analysis Methods 0.000 description 4
- 238000002790 cross-validation Methods 0.000 description 3
- 238000001035 drying Methods 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 238000010200 validation analysis Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 239000004459 forage Substances 0.000 description 2
- 239000000203 mixture Substances 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000011002 quantification Methods 0.000 description 2
- 230000005855 radiation Effects 0.000 description 2
- JVTAAEKCZFNVCJ-UHFFFAOYSA-M Lactate Chemical compound CC(O)C([O-])=O JVTAAEKCZFNVCJ-UHFFFAOYSA-M 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 239000012491 analyte Substances 0.000 description 1
- 239000000538 analytical sample Substances 0.000 description 1
- 235000021329 brown rice Nutrition 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 125000000524 functional group Chemical group 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000031700 light absorption Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 244000005700 microbiome Species 0.000 description 1
- 238000004476 mid-IR spectroscopy Methods 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 238000010298 pulverizing process Methods 0.000 description 1
- 239000010453 quartz Substances 0.000 description 1
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N silicon dioxide Inorganic materials O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 1
- 239000002904 solvent Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
- G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
- G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/02—Food
- G01N33/10—Starch-containing substances, e.g. dough
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
- G01N1/286—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q involving mechanical work, e.g. chopping, disintegrating, compacting, homogenising
- G01N2001/2873—Cutting or cleaving
Abstract
Description
본 발명은 근적외선 분광기를 이용하여 이탈리안 라이그라스 사일리지의 pH, 수분함량 및 젖산함량을 분석하는 방법에 관한 것으로, 시료의 전처리 없이 비파괴분석이 가능하여 빠르고 용이하게 측정할 수 있는 이점이 있다.The present invention relates to a method for analyzing the pH, moisture content and lactic acid content of Italian rice silage using a near infrared ray spectroscope, and it is possible to perform nondestructive analysis without pretreatment of a sample, thereby making it possible to measure quickly and easily.
일반적으로 근적외선은 가시광 영역의 장파장 영역에서 중적외선(Mid-IR) 영역의 단파장까지를 의미한다. 근적외선 분광분석법은 근적외선 영역에서의 빛 흡수에 입각한 분석법으로 어떤 분자결합에 근적외선이 조사되면 그 분자결합이 가지고 있는 고유한 진동에너지에 해당하는 복사선이 흡수된다. 결국 근적외선이 가지고 있는 고유한 진동에너지에 해당하는 복사선이 흡수된다. 따라서, 근적외선은 2500 nm 이상의 중적외선 영역에서 흡수되는 O-H, C-H, N-H, C=O, N-H 등과 같은 각 관능기가 갖는 기본 진동의 1, 2, 3차 배음(overtone)과 결합음(combination)에 해당하는 진동음이 나타나는 영역이다.In general, the near-infrared ray means from the long wavelength region of the visible light region to the short wavelength region of the mid-IR region. NIR spectroscopy is a method based on absorption of light in the near infrared region. When a near infrared ray is irradiated to a molecular bond, radiation corresponding to the inherent vibration energy of the molecular bond is absorbed. As a result, the radiation corresponding to the inherent vibration energy of the near infrared rays is absorbed. Therefore, the near-infrared rays have the first, second and third harmonic overtones and combinations of the basic vibrations of each functional group such as OH, CH, NH, C = O, and NH absorbed in the mid infrared region of 2500 nm or more This is the area where the corresponding vibration sound appears.
근적외선 분광분석법(NIR)의 최대 특징은 시료의 전처리가 필요 없거나, 최소한의 시료 전처리(예; 분쇄)만으로도 분석이 가능하며, 더 나아가 분쇄하지 않은 비파괴 상태로도 분석이 가능하다. 또한, 근적외선 분광분석법(NIR)은 수분이 흡수되지 않은 석영 재질을 이용한 시료장치를 쓸 수 있는 장점도 있다.The greatest feature of near infrared spectroscopy (NIR) is that it does not require sample pretreatment or can be analyzed with minimal sample preprocessing (eg crushing), and furthermore it can be analyzed in non-destructive state without crushing. In addition, near infrared spectroscopy (NIR) has the advantage of being able to use a sample device using a quartz material in which moisture is not absorbed.
통상적으로, 시료의 성분 및 특성 분석은 크게 구분하여 분석 대상물의 형태나 특성을 변화시켜 분석하는 파괴적인 방법과 형태나 특성을 변화시키지 않고 분석하는 비파괴적 방법으로 구분할 수 있다. 파괴적 방법에 의한 성분 및 특성 분석은 습식분석 혹은 화학분석으로 표현되며, 시료의 분쇄, 평량, 함수율 측정, 용액이나 용매를 이용한 화학반응, 성분검출, 정량 등 수차례의 단계적 과정이 요구된다. 또한, 짧게는 몇 분에서 길게는 며칠 동안의 복잡한 작업과정이 요구되고, 경우에 따라서는 고가의 분석장비와 많은 노동력이 요구되며, 최근에는 분석 폐기물에 의한 환경오염이 대두되고 있다. 그러나, 비파괴적 성분 및 특성 분석은 기기의 측정 센서를 분석 대상물의 표면에 접촉시키거나 비접촉시켜 측정하는 방법으로 기존 화학분석 혹은 파괴적 방법을 대신할 수 있는 안전한 분석 기술이다. 경우에 따라 파괴적 방법 및 화학 분석에 비해 정확도나 정밀도가 감소되는 경우도 있지만, 분석에 소요되는 시간, 경비 및 노력을 절감하고, 분석시료의 재사용 및 유지를 위해 다양한 방법의 비파괴적 분석 기술들이 개발되고 있다.In general, the analysis of components and characteristics of a sample can be roughly divided into a destructive method of analyzing the shape and characteristics of an analyte and a non-destructive method of analyzing the shape and characteristics without changing its characteristics. Analysis of components and characteristics by destructive method is expressed by wet analysis or chemical analysis. It requires several steps such as crushing, basis weight, water content, chemical reaction using solution or solvent, component detection and quantification. In addition, complex work processes are required for a few minutes to several days, in short, expensive analytical equipment and labor are required in some cases. Recently, environmental pollution caused by analytical waste is emerging. However, non-destructive component and characteristic analysis is a safe analytical technique that can replace conventional chemical analysis or destructive method by measuring the measurement sensor of the apparatus by contacting or non-contacting the surface of the object to be analyzed. In some cases, accuracy and precision may be reduced compared to destructive methods and chemical analysis. However, various non-destructive analysis techniques have been developed to reduce the time, expense and effort required for analysis and to reuse and maintain analytical samples. .
한국공개특허 제2006-0019961호에는 근적외선 분광법을 이용한 현미 내 찹쌀함량의 측정방법이 개시되어 있으나, 본 발명의 근적외선 분광기를 이용한 이탈리안 라이그라스 사일리지의 pH, 수분함량 및 젖산함량 측정 방법과는 상이하다.Korean Patent Laid-Open No. 2006-0019961 discloses a method for measuring the content of glutinous rice in brown rice using near infrared spectroscopy, but is different from the method for measuring the pH, moisture content and lactic acid content of Italian rice silage using the near-infrared spectroscope of the present invention .
본 발명은 상기와 같은 요구에 의해 도출된 것으로서, 기존에는 생 시료를 1~2일 동안 건조 후 분쇄하여 NIR 스펙트럼을 수집함으로 인해 많은 시간이 소요되는 단점이 있었는데, 본 발명에서는 이러한 단점을 해결하고자 건조 및 분쇄 과정을 생략하고 단지 2~3 cm 입자도로 절단하여 시료를 전처리하였다. 따라서, 본 발명은 근적외선 분광기를 이용하여 비파괴적으로 신속 용이하게 이탈리안 라이그라스 사일리지의 pH, 수분함량 및 젖산함량을 측정하는 방법을 제공하는 데 있다.SUMMARY OF THE INVENTION The present invention has been made in view of the above-mentioned needs, and there has been a disadvantage in that it takes a long time to collect the NIR spectrum by drying and pulverizing raw samples for 1 to 2 days. The drying and crushing procedures were omitted and the samples were cut into only 2 ~ 3 cm particle size and pretreated. Accordingly, the present invention is to provide a method for measuring the pH, moisture content and lactic acid content of Italian rice silage quickly and easily using a near-infrared spectroscope.
상기 과제를 해결하기 위해, 본 발명은 근적외선 분광기를 이용하여 이탈리안 라이그라스 사일리지의 pH, 수분함량 및 젖산함량을 분석하는 방법을 제공한다.In order to solve the above problems, the present invention provides a method for analyzing the pH, moisture content and lactic acid content of Italian rice silage using a near-infrared spectroscope.
본 발명은 근적외선 분광기를 사용하여 이탈리안 라이그라스 사일리지의 pH, 수분함량 및 젖산함량을 건조 또는 분쇄하는 전처리 없이 생시료 자체로 비파괴분석이 가능하여, 빠르고 용이하게 측정할 수 있는 이점이 있다. 또한, 신속한 조사료 품질평가로 국내산 조사료의 투명한 유통체계를 확립하고, 유통활성화를 촉진시킬 수 있다.INDUSTRIAL APPLICABILITY The present invention is capable of nondestructive analysis by using a near infrared ray spectroscope without pretreatment for drying or crushing the pH, moisture content and lactic acid content of Italian rice silage, thereby enabling quick and easy measurement. In addition, rapid evaluation of the quality of the forage can establish a transparent distribution system of domestic forage and promote the activation of distribution.
도 1은 이탈리안 라이그라스 사일리지의 근적외선 스펙트럼 측정을 위한 시료 전처리 방법을 나타낸 것이다.
도 2는 이탈리안 라이그라스 사일리지의 근적외선 스펙트럼을 나타낸 그래프와 이탈리안 라이그라스 사일리지의 근적외선 스펙트럼을 미분 처리한 후의 그래프를 나타낸 것이다.
도 3은 근적외선 분광기를 이용하여 얻어진 수분 함량을 기준값(reference value)과 비교한 그래프이다.
도 4는 근적외선 분광기를 이용하여 얻어진 pH를 기준값(reference value)과 비교한 그래프이다.
도 5는 근적외선 분광기를 이용하여 얻어진 젖산함량을 기준값(reference value)과 비교한 그래프이다.1 shows a sample pretreatment method for measurement of near-infrared spectrum of Italian rice silage.
FIG. 2 is a graph showing the near-infrared spectrum of Italian Ragasil silage and a graph after finishing the near-infrared spectrum of Italian Ragasil silage.
3 is a graph comparing the moisture content obtained using a near-infrared spectroscope with a reference value.
4 is a graph comparing the pH obtained using a near-infrared spectroscope with a reference value.
5 is a graph comparing the content of lactic acid obtained using a near-infrared spectroscope with a reference value.
본 발명의 목적을 달성하기 위하여, 본 발명은 (a) 이탈리안 라이그라스 사일리지 시료의 근적외선 스펙트럼을 획득하는 단계;In order to accomplish the object of the present invention, the present invention provides a method for producing a microorganism, comprising the steps of: (a) obtaining a near-infrared spectrum of an Italian rice silage sample;
(b) 상기 근적외선 스펙트럼의 측정 파장별 갭을 4 nm 또는 8 nm로 하여 수학적으로 전처리하고, 상기 전처리된 근적외선 스펙트럼에 1차 미분법을 적용하여 수처리하며, 상기 수처리된 근적외선 스펙트럼을 부분최소자승법(Partial Least Square: PLS)으로 회귀분석하여 검량식을 유도하는 단계;(b) mathematically preprocessing the gap of the measured wavelength of the near-infrared spectrum at 4 nm or 8 nm, applying a first order differentiation method to the pretreated near-infrared spectrum, and performing the water treatment by using a partial least squares method Least Square (PLS);
(c) 상기 검량식을 확인하는 단계; 및(c) confirming the calibration equation; And
(d) 상기 검량식을 이용하여 상기 이탈리안 라이그라스 사일리지의 pH, 수분함량 또는 젖산함량을 정량분석하는 단계를 포함하는 근적외선 분광법을 이용한 이탈리안 라이그라스 사일리지의 분석방법을 제공한다.(d) quantitatively analyzing the pH, moisture content or lactic acid content of the Italian rice silage using the calibration formula, using the near-infrared spectroscopic method.
본 발명의 분석방법에서, 상기 (a)단계의 근적외선 스펙트럼 획득은 근적외선 분광기를 이용하여 획득할 수 있는데, 본 발명에서 사용하는 근적외선 분광기는 파장대역이 680~2500 nm으로, 다른 파장대역보다 시료의 전처리 없이 있는 그대로 측정하기가 용이하며, 시약 등을 사용하지 않을 뿐만 아니라 시료의 정량, 혼합, 가열, 추출 등의 과정의 전처리 과정이 필요 없어 신속하고, 측정시료의 손상없이 회수하여 이용 및 기타분석에 사용할 수 있는 장점이 있다.In the analysis method of the present invention, the near infrared ray spectroscopy of step (a) can be obtained by using a near-infrared ray spectroscope. The near infrared ray spectroscope used in the present invention has a wavelength band of 680 to 2500 nm, It is easy to measure as it is without pretreatment. It does not use reagents, but it does not require preprocessing process such as quantification, mixing, heating and extraction of samples. There is an advantage that can be used.
본 발명의 근적외선 스펙트럼은 본 분야에서 통상적으로 사용하는 모든 방식으로 스캐닝하여 얻을 수 있으며, 바람직하게는 반사투과 방식으로 얻을 수 있다.The near-infrared spectrum of the present invention can be obtained by scanning in any manner commonly used in the art, and preferably obtained by a reflection transmission method.
또한, 본 발명의 분석방법에서, 얻어진 스펙트럼은 복잡하고 흡광 봉우리들의 겹침과 물리적인 성질에 변화를 주는 확인되지 않은 변화요인들이 존재하게 된다. 이러한 문제점을 해결하기 위해 상기 스펙트럼을 수학적 전처리하는 과정 후 회귀분석을 통하여 검량식을 유도하는 것이 바람직하다. 따라서, 본 발명에서는 얻어진 스펙트럼을 오버-피팅(over-fitting) 등 스펙트럼이 잘못 해석되는 것을 방지하기 위하여 측정 파장별 갭(gap)을 조절하는데, 본 방법에서는 수분함량 또는 pH를 분석하는 경우 4 nm로 갭을 조절하는 것이 바람직하고, 젖산함량을 분석하는 경우 8 nm로 갭을 조절하는 것이 바람직하다.Further, in the analysis method of the present invention, the obtained spectrum is complicated, and there are unidentified change factors that cause changes in the overlapping and physical properties of the light-absorbing peaks. To solve this problem, it is preferable to derive the calibration equation through regression analysis after the mathematical preprocessing of the spectrum. Accordingly, in the present invention, to prevent the spectra from being misinterpreted such as over-fitting of the obtained spectrum, the gap according to the measurement wavelength is adjusted. In this method, when analyzing the moisture content or pH, It is preferable to adjust the gap to 8 nm when analyzing the content of lactic acid.
또한, 본 발명의 분석방법에서, 상기 (b)단계의 수처리는 W-X-Y(W는 미분 회수이며, X는 스펙트럼의 nm 측정 파장 갭(gap)이며, Y는 파장 갭 수처리에서 스펙트럼의 연결을 부드럽게 하기 위한 수처리를 의미함)로 표시되고, 수분함량 또는 pH의 경우 1-4-4을 적용하고, 젖산함량의 경우 1-8-8을 적용하는 것이 바람직하다.Also, in the analysis method of the present invention, the water treatment in the step (b) may be performed by WXY (where W is the number of differentiation, X is the nm measured wavelength gap of the spectrum, Y is the wavelength of the spectrum, , It is preferable to apply 1-4-4 in case of moisture content or pH and 1-8-8 in case of lactic acid content.
또한, 본 발명의 분석방법에서, 수처리 후 회귀분석을 수행하여 검량식을 유도하게 되는데, 상기 회귀분석은 부분최소자승법(Partial least square: PLS)을 통해 적정 검량식을 유도할 수 있다.Also, in the analysis method of the present invention, a calibration equation is derived by performing regression analysis after water treatment. The regression analysis can derive a proper calibration equation through partial least squares (PLS).
또한, 본 발명의 분석방법에서, 상기 (c)단계의 검량식 확인은 교차 검증(cross validation)을 통해 확인할 수 있다.Also, in the analysis method of the present invention, verification of the calibration equation of step (c) may be confirmed through cross validation.
본 발명은 또한,The present invention also relates to
(a) 이탈리안 라이그라스 사일리지 시료의 근적외선 스펙트럼을 획득하는 단계;(a) obtaining a near-infrared spectrum of an Italian raglas silage sample;
(b) 상기 근적외선 스펙트럼의 측정 파장별 갭을 4 nm로 하여 수학적으로 전처리하고, 상기 전처리된 근적외선 스펙트럼에 1차 미분법을 적용하여 수처리하며, 상기 수처리된 근적외선 스펙트럼을 부분최소자승법(Partial Least Square: PLS)으로 회귀분석하여 검량식을 유도하는 단계;(b) mathematically preprocessing the gap of the measured wavelength of the near-infrared spectrum to 4 nm, applying a first order differentiation method to the pretreated near-infrared spectrum, and treating the water-treated near infrared spectrum with a partial least square method, PLS) to derive a calibration equation;
(c) 상기 검량식을 확인하는 단계; 및(c) confirming the calibration equation; And
(d) 상기 검량식을 이용하여 시료를 정량분석하는 단계를 포함하는 이탈리안 라이그라스 사일리지의 pH 측정 방법을 제공한다.(d) quantitatively analyzing the sample using the calibration equation.
본 발명은 또한,The present invention also relates to
(a) 이탈리안 라이그라스 사일리지 시료의 근적외선 스펙트럼을 획득하는 단계;(a) obtaining a near-infrared spectrum of an Italian raglas silage sample;
(b) 상기 근적외선 스펙트럼의 측정 파장별 갭을 4 nm로 하여 수학적으로 전처리하고, 상기 전처리된 근적외선 스펙트럼에 1차 미분법을 적용하여 수처리하며, 상기 수처리된 근적외선 스펙트럼을 부분최소자승법(Partial Least Square: PLS)으로 회귀분석하여 검량식을 유도하는 단계;(b) mathematically preprocessing the gap of the measured wavelength of the near-infrared spectrum to 4 nm, applying a first order differentiation method to the pretreated near-infrared spectrum, and treating the water-treated near infrared spectrum with a partial least square method, PLS) to derive a calibration equation;
(c) 상기 검량식을 확인하는 단계; 및(c) confirming the calibration equation; And
(d) 상기 검량식을 이용하여 시료를 정량분석하는 단계를 포함하는 이탈리안 라이그라스 사일리지에 포함된 수분함량의 측정 방법을 제공한다.and (d) quantitatively analyzing the sample using the calibration equation. The method for measuring the moisture content of the Italian rice silage includes:
본 발명은 또한,The present invention also relates to
(a) 이탈리안 라이그라스 사일리지 시료의 근적외선 스펙트럼을 획득하는 단계;(a) obtaining a near-infrared spectrum of an Italian raglas silage sample;
(b) 상기 근적외선 스펙트럼의 측정 파장별 갭을 8 nm로 하여 수학적으로 전처리하고, 상기 전처리된 근적외선 스펙트럼에 1차 미분법을 적용하여 수처리하며, 상기 수처리된 근적외선 스펙트럼을 부분최소자승법(Partial Least Square: PLS)으로 회귀분석하여 검량식을 유도하는 단계;(b) mathematically preprocessing the gap of the measured wavelength of the near-infrared spectrum to 8 nm, applying a first order differential method to the pre-processed near-infrared spectrum, and treating the water-treated near infrared spectrum with a partial least square method, PLS) to derive a calibration equation;
(c) 상기 검량식을 확인하는 단계; 및(c) confirming the calibration equation; And
(d) 상기 검량식을 이용하여 시료를 정량분석하는 단계를 포함하는 이탈리안 라이그라스 사일리지에 포함된 젖산함량의 측정 방법을 제공한다.
(d) quantitatively analyzing the sample using the calibration equation. The method for measuring the content of lactic acid contained in the Italian rice silage is also provided.
이하, 본 발명을 실시예에 의해 상세히 설명한다. 그러나, 하기 실시예는 본 발명을 예시하는 것일 뿐, 본 발명의 내용이 하기 실시예에 한정되는 것이 아니다.
Hereinafter, the present invention will be described in detail by way of examples. However, the following examples are illustrative of the present invention, and the contents of the present invention are not limited to the following examples.
실시예 1: 근적외선을 이용한 이탈리안 라이그라스 사일리지의 성분 분석Example 1 Analysis of Components of Italian Ryegrass Silage Using Near Infrared
1. 시료준비 및 근적외선 스펙트럼 획득1. Sample preparation and acquisition of near infrared spectrum
시료는 2010~2011년까지 전국 이탈리안 라이그라스 사일리지 조제 농가로부터 240점을 수집하였다. 수집한 이탈리안 라이그라스 사일리지를 가위로 2~3 cm로 절단한 후 100~150 g 정도를 시료컵에 충진한 후 근적외선 분광분석기에 장착하여 스캐닝하였으며, 측정방식은 반사투과법을 사용하였고, 1 nm 간격으로 스캐닝을 실시하였다(도 1). 그리고 근적외선 파장대역은 680~2500 nm에서 실시하였다.The sample collected 240 points from the national Italian rice silage farmhouse from 2010 to 2011. The collected Italian ragass silage was cut into 2 ~ 3 cm with scissors, and filled in a sample cup of about 100 ~ 150 g. Then, it was mounted on a NIR spectroscope and scanned. Scanning was performed at intervals (Fig. 1). The near infrared wavelength band was measured at 680 ~ 2500 nm.
이와 같은 방법으로 계산된 이탈리안 라이그라스 사일리지의 pH, 수분함량 및 젖산함량은 하기 표 1에 나타내었고, 도 2의 왼쪽 그래프와 같은 스펙트럼을 얻을 수 있었다.The pH, moisture content and lactic acid content of the Italian Ryegrass silage thus calculated are shown in the following Table 1, and the spectrum shown in the left graph of FIG. 2 was obtained.
2. 수처리 및 회귀분석2. Water treatment and regression analysis
수처리는 미분법을 나타내는 것으로 가장 많이 사용되는 스펙트럼 전처리 기법으로 스펙트럼을 미분하여 흡수대의 변화를 강조함으로써 스펙트럼의 변화는 증폭되고, 동시에 변화만 보기 때문에 바탕선이 움직여도 관계가 없다. 즉, 미분으로 도 2의 오른쪽 그래프와 같이 바탕선 변화가 제거된다. 상기 스캐닝한 근적외선 스펙트럼에 1차 또는 2차 미분법을 적용하여 수처리하였다. 수처리의 W-X-Y에서 W는 미분 회수이며, X는 스펙트럼의 nm 측정 파장 갭(gap), Y는 파장 갭 수처리에서 스펙트럼의 연결을 부드럽게 하기 위한 수처리를 의미한다. 상기 수처리된 근적외선 스펙트럼을 계량분석화학(Chemometrics)을 이용하여 부분최소자승법(Partial Least Square: PLS)으로 회귀분석하여 검량식을 유도하였다.The water treatment represents the differentiation method, and the spectrum pre-processing technique which is used most often is differentiated by emphasizing the change of the absorption band by differentiating the spectrum and amplifying the spectrum change. That is, the base line change is removed by differential as shown in the right graph of FIG. The scanned near infrared spectrum was subjected to water treatment by applying primary or secondary differential method. In W-X-Y of water treatment, W is the number of differentiation, X is the nm measurement wavelength gap of the spectrum, and Y is the water treatment to soften the connection of the spectrum in the wavelength gap water treatment. The water-treated NIR spectra were regression analyzed by Partial Least Square (PLS) using Chemometrics to derive a calibration equation.
하기 표 2는 이탈리안 라이그라스 사일리지의 수분함량 분석을 위한 검량식 작성의 결과이다. 하기 표 2와 같이 1-4-4를 통한 검량식이 결정계수(R2)가 높으면서 오차가 낮게 나타나, 가장 우수한 것으로 판단되었다. 도 3은 실험실 내에서 표준분석하여 얻어진 수분 함량(기준값: Ref values)과 근적외선 분광기를 통해 도출된 검량식에서 얻어진 수분 분석치와의 상관관계를 나타낸 것으로, 거의 일치하여 검량식의 정확성을 확인할 수 있었다.Table 2 below shows the results of the calibration formula for analyzing the moisture content of Italian rice silage. As shown in the following Table 2, the calibration formula through 1-4-4 showed a high coefficient of determination (R 2 ) and a low error, and was considered to be the most excellent. FIG. 3 shows the correlation between the moisture content (reference value: Ref values) obtained by standard analysis in the laboratory and the moisture analysis value obtained from the calibration formula derived through the near infrared ray spectroscopy, and the accuracy of the calibration equation was confirmed.
SEC: 검량식 작성 표준오차SEC: calibration equation standard error
SECV: 작성된 검량식의 상호검증 오차
SECV: Mutual verification error of the prepared calibration equation
하기 표 3은 수처리에 따른 이탈리안 라이그라스 사일리지의 pH 분석을 위한 검량식 작성의 결과이다. 하기 표 3과 같이 1-4-4를 통한 검량식이 결정계수(R2)가 높게 나타나, 가장 우수한 것으로 판단되었다. 도 4는 실험실 내에서 표준분석하여 얻어진 pH(Ref values)와 근적외선 분광기를 통해 도출된 검량식에서 얻어진 pH 분석치와의 상관관계를 나타낸 것으로, 거의 일치하여 검량식의 정확성을 확인할 수 있었다.Table 3 shows the result of the calibration formula for the pH analysis of Italian rice silage according to the water treatment. As shown in Table 3 below, the calibration coefficient (R 2 ) of the calibration formula through 1-4-4 was high, and it was judged to be the most excellent. FIG. 4 shows the correlation between the pH value obtained by standard analysis in the laboratory and the pH value obtained from the calibration equation derived through the near-infrared spectroscopy, and the accuracy of the calibration equation was confirmed by almost matching.
SEC: 검량식 작성 표준오차SEC: calibration equation standard error
SECV: 작성된 검량식의 상호검증 오차
SECV: Mutual verification error of the prepared calibration equation
하기 표 4는 수처리에 따른 이탈리안 라이그라스 사일리지의 젖산 성분분석을 위한 검량식 작성의 결과이다. 하기 표 4와 같이 1-8-8를 통한 검량식이 결정계수(R2)가 가장 높게 나타나, 가장 우수한 것으로 판단되었다. 도 5는 실험실 내에서 표준분석하여 얻어진 젖산함량(Ref values)과 근적외선 분광기를 통해 도출된 검량식에서 얻어진 젖산 분석치와의 상관관계를 나타낸 것으로, 거의 일치하여 검량식의 정확성을 확인할 수 있었다.Table 4 shows the result of the calibration formula for analysis of lactic acid component of Italian rice silage according to water treatment. As shown in Table 4, the calibration coefficient (R 2 ) of the calibration formula through 1-8-8 was the highest, and it was judged to be the most excellent. FIG. 5 shows the correlation between the lactate content (Ref values) obtained by the standard analysis in the laboratory and the lactic acid analysis value obtained from the calibration formula derived through the near-infrared spectroscopy, and the accuracy of the calibration equation was confirmed.
SEC: 검량식 작성 표준오차SEC: calibration equation standard error
SECV: 작성된 검량식의 상호검증 오차
SECV: Mutual verification error of the prepared calibration equation
3. 검증3. Verification
검량식이 얻어지면 이 검량식의 적용이 합당한지를 검증하여야 한다. 따라서 검량식을 만들 때 교차검증(cross validation: CV)을 사용하여 검량 세트 내부 검증을 하였다. 이 방법은 검량세트 내 시료 중 일부는 검량식을 만드는데 이용되고, 검량식 작성에 제외된 시료들을 대상으로 별도의 예측세트(Prediction set)를 만들어 검량식을 검증하는 것이다.Once the calibration equation is obtained, it should be verified that the application of the calibration equation is reasonable. Therefore, we used the cross validation (CV) to verify the calibration set when making the calibration equation. In this method, some of the samples in the calibration set are used to make calibration formulas, and the calibration formulas are verified by creating a separate prediction set (Prediction set) for the samples excluded from the calibration formulation.
이와 같은 근적외선 분광분석기를 이용한 이탈리안 라이그라스 사일리지의 pH, 수분함량 또는 젖산함량 측정 방법에 따르면, 수처리는, 수분함량 또는 pH의 경우 1-4-4를 통한 검량식이 바람직하며, 젖산함량의 경우 1-8-8을 통한 검량식이 바람직하였다. 또한, 적정 검량식을 도출하기 위한 회귀식 작성은 부분최소자승법(Partial Least Square: PLS)이 가장 적합하였다.According to the method of measuring the pH, the moisture content or the lactic acid content of the Italian rice silage using the near infrared ray spectroscopic analyzer, it is preferable that the water treatment is performed by 1-4-4 in the case of water content or pH, -8-8 was preferred. Partial Least Square (PLS) was most suitable for the regression equation for deriving the appropriate calibration equation.
Claims (11)
(b) 상기 근적외선 스펙트럼의 측정 파장별 갭을 4 nm 또는 8 nm로 하여 수학적으로 전처리하고, 상기 전처리된 근적외선 스펙트럼에 1차 미분법을 적용하여 수처리하며, 상기 수처리된 근적외선 스펙트럼을 부분최소자승법(Partial Least Square: PLS)으로 회귀분석하여 검량식을 유도하는 단계;
(c) 상기 검량식을 확인하는 단계; 및
(d) 상기 검량식을 이용하여 상기 이탈리안 라이그라스 사일리지의 pH, 수분 함량 또는 젖산함량을 정량분석하는 단계를 포함하는 근적외선 분광법을 이용한 이탈리안 라이그라스 사일리지의 분석방법.(a) obtaining a near-infrared spectrum of an Italian raglas silage sample;
(b) mathematically preprocessing the gap of the measured wavelength of the near-infrared spectrum at 4 nm or 8 nm, applying a first order differentiation method to the pretreated near-infrared spectrum, and performing the water treatment by using a partial least squares method Least Square (PLS);
(c) confirming the calibration equation; And
(d) quantitatively analyzing the pH, moisture content, or lactic acid content of the Italian rice silage using the calibration formula, using the near infrared spectroscopic method.
(b) 상기 근적외선 스펙트럼의 측정 파장별 갭을 4 nm로 하여 수학적으로 전처리하고, 상기 전처리된 근적외선 스펙트럼에 1차 미분법을 적용하여 수처리하며, 상기 수처리된 근적외선 스펙트럼을 부분최소자승법(Partial Least Square: PLS)으로 회귀분석하여 검량식을 유도하는 단계;
(c) 상기 검량식을 확인하는 단계; 및
(d) 상기 검량식을 이용하여 시료를 정량분석하는 단계를 포함하는 이탈리안라이그라스 사일리지의 pH 측정 방법.(a) obtaining a near-infrared spectrum of an Italian raglas silage sample;
(b) mathematically preprocessing the gap of the measured wavelength of the near-infrared spectrum to 4 nm, applying a first order differentiation method to the pretreated near-infrared spectrum, and treating the water-treated near infrared spectrum with a partial least square method, PLS) to derive a calibration equation;
(c) confirming the calibration equation; And
(d) quantitatively analyzing the sample using the calibration equation.
(b) 상기 근적외선 스펙트럼의 측정 파장별 갭을 4 nm로 하여 수학적으로 전처리하고, 상기 전처리된 근적외선 스펙트럼에 1차 미분법을 적용하여 수처리하며, 상기 수처리된 근적외선 스펙트럼을 부분최소자승법(Partial Least Square: PLS)으로 회귀분석하여 검량식을 유도하는 단계;
(c) 상기 검량식을 확인하는 단계; 및
(d) 상기 검량식을 이용하여 시료를 정량분석하는 단계를 포함하는 이탈리안라이그라스 사일리지에 포함된 수분함량의 측정 방법.(a) obtaining a near-infrared spectrum of an Italian raglas silage sample;
(b) mathematically preprocessing the gap of the measured wavelength of the near-infrared spectrum to 4 nm, applying a first order differentiation method to the pretreated near-infrared spectrum, and treating the water-treated near infrared spectrum with a partial least square method, PLS) to derive a calibration equation;
(c) confirming the calibration equation; And
(d) quantitatively analyzing the sample using the calibration equation. < Desc / Clms Page number 19 >
(b) 상기 근적외선 스펙트럼의 측정 파장별 갭을 8 nm로 하여 수학적으로 전처리하고, 상기 전처리된 근적외선 스펙트럼에 1차 미분법을 적용하여 수처리하며, 상기 수처리된 근적외선 스펙트럼을 부분최소자승법(Partial Least Square: PLS)으로 회귀분석하여 검량식을 유도하는 단계;
(c) 상기 검량식을 확인하는 단계; 및
(d) 상기 검량식을 이용하여 시료를 정량분석하는 단계를 포함하는 이탈리안라이그라스 사일리지에 포함된 젖산함량의 측정 방법.(a) obtaining a near-infrared spectrum of an Italian raglas silage sample;
(b) mathematically preprocessing the gap of the measured wavelength of the near-infrared spectrum to 8 nm, applying a first order differentiation method to the pretreated near-infrared spectrum and water-treating the water-treated near infrared spectrum by a partial least square method, PLS) to derive a calibration equation;
(c) confirming the calibration equation; And
(d) quantitatively analyzing the sample using the calibration equation. < Desc / Clms Page number 19 >
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020140067072A KR20140078597A (en) | 2014-06-02 | 2014-06-02 | Method for measuring pH, water content and lactic acid content of Italian ryegrass silage using near infrared spectroscopy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020140067072A KR20140078597A (en) | 2014-06-02 | 2014-06-02 | Method for measuring pH, water content and lactic acid content of Italian ryegrass silage using near infrared spectroscopy |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| KR1020120060238A Division KR20130136670A (en) | 2012-06-05 | 2012-06-05 | Method for measuring ph, water content and lactic acid content of italian ryegrass silage using near infrared spectroscopy |
Related Child Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| KR20150044548A Division KR20150039739A (en) | 2015-03-30 | 2015-03-30 | Method for measuring pH, water content and lactic acid content of Italian ryegrass silage using near infrared spectroscopy |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| KR20140078597A true KR20140078597A (en) | 2014-06-25 |
Family
ID=51130145
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| KR1020140067072A KR20140078597A (en) | 2014-06-02 | 2014-06-02 | Method for measuring pH, water content and lactic acid content of Italian ryegrass silage using near infrared spectroscopy |
Country Status (1)
| Country | Link |
|---|---|
| KR (1) | KR20140078597A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101594954B1 (en) * | 2015-03-17 | 2016-02-18 | 대한민국 | Analytical method using infrared rays for examining the content of dried frozen red pepper in red pepper powder |
| CN111077104A (en) * | 2019-12-26 | 2020-04-28 | 广州傲农生物科技有限公司 | Method for rapidly determining total acid content in fermented feed |
| CN114624402A (en) * | 2022-01-28 | 2022-06-14 | 广西壮族自治区水产科学研究院 | Snail rice noodle sour bamboo shoot quality evaluation method based on near infrared spectrum |
-
2014
- 2014-06-02 KR KR1020140067072A patent/KR20140078597A/en active Application Filing
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101594954B1 (en) * | 2015-03-17 | 2016-02-18 | 대한민국 | Analytical method using infrared rays for examining the content of dried frozen red pepper in red pepper powder |
| CN111077104A (en) * | 2019-12-26 | 2020-04-28 | 广州傲农生物科技有限公司 | Method for rapidly determining total acid content in fermented feed |
| CN114624402A (en) * | 2022-01-28 | 2022-06-14 | 广西壮族自治区水产科学研究院 | Snail rice noodle sour bamboo shoot quality evaluation method based on near infrared spectrum |
| CN114624402B (en) * | 2022-01-28 | 2023-06-27 | 广西壮族自治区水产科学研究院 | Quality evaluation method for snail rice noodle sour bamboo shoots based on near infrared spectrum |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109085136B (en) | Method for measuring content of oxide components in cement raw material by near-infrared diffuse reflection spectrum | |
| CN105319198B (en) | Benzene content in gasoline Forecasting Methodology based on Raman spectrum analytic technique | |
| Cooper et al. | Potential of near-infrared spectroscopy to characterize wood products | |
| Bonifazi et al. | Modeling color and chemical changes on normal and red heart beech wood by reflectance spectrophotometry, Fourier Transform Infrared spectroscopy and hyperspectral imaging | |
| JPH0447254A (en) | Method and apparatus for measuring content of component of skim milk, milk, cream and cheese by using near infrared rays | |
| Sandak et al. | Estimation of physical and mechanical properties of timber members in service by means of infrared spectroscopy | |
| Bonifazi et al. | A new approach for the modelling of chestnut wood photo-degradation monitored by different spectroscopic techniques | |
| CN103091274A (en) | Method for determining content of water in Salvianolic acid for injection through near-infrared diffuse reflection spectrometry | |
| KR20140078597A (en) | Method for measuring pH, water content and lactic acid content of Italian ryegrass silage using near infrared spectroscopy | |
| Anyidoho et al. | Nondestructive authentication of the regional and geographical origin of cocoa beans by using a handheld NIR spectrometer and multivariate algorithm | |
| CN109374548A (en) | A method of quickly measuring nutritional ingredient in rice using near-infrared | |
| CN105486663B (en) | A method of detecting the stable carbon isotope ratio of soil using near infrared spectrum | |
| Henriques et al. | Determination of formaldehyde/urea molar ratio in amino resins by near‐infrared spectroscopy | |
| Dai et al. | On-line UV-NIR spectroscopy as a process analytical technology (PAT) tool for on-line and real-time monitoring of the extraction process of Coptis Rhizome | |
| KR20150039738A (en) | Method for measuring content of crude protein, acid detergent fiber and neutral detergent fiber of Italian ryegrass silage using near infrared spectroscopy | |
| Diesel et al. | Near-infrared spectroscopy and wavelength selection for estimating basic density in Mimosa tenuiflora [Willd.] Poiret wood | |
| KR20150039739A (en) | Method for measuring pH, water content and lactic acid content of Italian ryegrass silage using near infrared spectroscopy | |
| CN102928356A (en) | Method for measuring essence solvent content rapidly | |
| CN104568828A (en) | Method for determining tensile strength of reproduced tobacco leaves of papermaking method by near-infrared diffuse reflection spectrum | |
| Cao et al. | Potential of near-infrared spectroscopy to detect defects on the surface of solid wood boards | |
| KR20140078596A (en) | Method for measuring content of crude protein, acid detergent fiber and neutral detergent fiber of Italian ryegrass silage using near infrared spectroscopy | |
| Dong et al. | Nondestructive method for analysis of the soybean quality | |
| KR20130136668A (en) | Method for measuring content of crude protein, acid detergent fiber and neutral detergent fiber of italian ryegrass silage using near infrared spectroscopy | |
| KR20130136670A (en) | Method for measuring ph, water content and lactic acid content of italian ryegrass silage using near infrared spectroscopy | |
| CN109164058A (en) | A kind of method of infrared spectroscopy FTIR detection clarifier NX8000 content |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| A107 | Divisional application of patent | ||
| A201 | Request for examination | ||
| E902 | Notification of reason for refusal | ||
| E601 | Decision to refuse application | ||
| A107 | Divisional application of patent |