CN107167447A - The method for blending apple fumet content in cider is calculated using near-infrared spectrum technique - Google Patents
The method for blending apple fumet content in cider is calculated using near-infrared spectrum technique Download PDFInfo
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Abstract
Description
技术领域technical field
本发明涉及果汁原汁检测领域，特别是一种采用近红外光谱技术计算勾兑苹果汁中苹果原汁含量的方法。The invention relates to the field of fruit juice detection, in particular to a method for calculating the content of apple juice in blended apple juice by using near-infrared spectroscopy.
背景技术Background technique
苹果富含钾、类黄酮、抗氧化物，能有效降低高血压和冠心病的发生，广受消费者的喜爱。一些不法商家为了降低成本，贩卖的鲜榨苹果汁时常由果粉冲剂或者果粉冲剂勾兑鲜榨果汁等制成以达到欺瞒消费者目的，这种行为不仅侵犯消费者的权益更威胁消费者健康。传统果汁鉴别的方法是通过感官进行鉴定，但由于鉴定结果不可量化，同时具有一定的主观性和盲目性；而基于化学分析的果汁品质与含量鉴定，虽然具有精度高的优点，但存在鉴定周期长、鉴定过程繁琐、鉴定费用高等方面的不足。为此，研究一种简单、易于操作，并且能快速、准确鉴定鲜榨果汁含量的方法，对于食品安全具有重大的意义。Apples are rich in potassium, flavonoids, and antioxidants, which can effectively reduce the incidence of high blood pressure and coronary heart disease, and are widely loved by consumers. In order to reduce costs, some unscrupulous businesses often sell freshly squeezed apple juice made of fruit powder granules or fruit powder granules blended with fresh juice to deceive consumers. This behavior not only violates the rights of consumers but also threatens their health. The traditional method of fruit juice identification is through sensory identification, but because the identification results are not quantifiable, it has a certain degree of subjectivity and blindness; while the identification of juice quality and content based on chemical analysis has the advantage of high accuracy, but there is an identification period. Long time, cumbersome identification process, high identification costs and other deficiencies. For this reason, it is of great significance for food safety to study a method that is simple, easy to operate, and can quickly and accurately identify the content of freshly squeezed fruit juice.
近红外光谱仪（Near Infrared Spectrum Instrument，NIRS）是介于可见光（Vis）和中红外（MIR）之间的电磁辐射波，近红外光谱区定义为780-2526nm的区域，是人们在吸收光谱中发现的第一个非可见光区。近红外光谱区与有机分子中含氢基团（O-H、N-H、C-H）振动的合频和各级倍频的吸收区一致，通过扫描样品的近红外光谱，可以得到样品中有机分子含氢基团的特征信息，而且利用近红外光谱技术分析样品具有方便、快速、高效、准确和成本较低，不破坏样品，不消耗化学试剂，不污染环境，仪器便携等优点，近年来已经被广泛的应用在食品安全的检测中。Near Infrared Spectrum Instrument (NIRS) is an electromagnetic radiation wave between visible light (Vis) and mid-infrared (MIR). The near-infrared spectral region is defined as the region of 780-2526nm. the first non-visible region. The near-infrared spectral region is consistent with the combination frequency of the vibration of hydrogen-containing groups (O-H, N-H, C-H) in organic molecules and the absorption region of multiple levels of frequency multiplication. By scanning the near-infrared spectrum of the sample, the hydrogen-containing groups of organic molecules in the sample can be obtained. In addition, the use of near-infrared spectroscopy to analyze samples has the advantages of convenience, speed, efficiency, accuracy, and low cost. It does not destroy samples, does not consume chemical reagents, does not pollute the environment, and is portable. It has been widely used in recent years. It is used in the detection of food safety.
在实际工作中，混合物中的含量通常难以检测，近红外光谱的定量依据主要是光谱本身，因为光谱反映了真实样品的组成和结构信息。因此，本发明通过已知样本建立偏最小二乘模型，建立物质间隐含的关系，测得相应物质的含量。In actual work, the content in the mixture is usually difficult to detect, and the quantitative basis of near-infrared spectroscopy is mainly the spectrum itself, because the spectrum reflects the composition and structure information of real samples. Therefore, the present invention establishes a partial least squares model through known samples, establishes an implicit relationship between substances, and measures the content of corresponding substances.
综上，当前急需一种快速、无损的勾兑苹果汁中苹果原汁含量计算方法，有利于人们辨别掺假，本发明提出了以近红外光谱为检测方法，同时利用偏最小二乘分析法实现对勾兑苹果汁中苹果原汁含量的测量。In summary, there is an urgent need for a fast and non-destructive method for calculating the content of apple juice in blended apple juice, which is beneficial for people to identify adulteration. Determination of apple juice content in blended apple juice.
发明内容Contents of the invention
有鉴于此，本发明的目的是提出一种采用近红外光谱技术计算勾兑苹果汁中苹果原汁含量的方法，过程快速，结果准确，并且对样品无损。In view of this, the object of the present invention is to propose a method for calculating the content of apple juice in blended apple juice by using near-infrared spectroscopy. The process is fast, the result is accurate, and the sample is not damaged.
本发明采用以下方案实现：一种采用近红外光谱技术计算勾兑苹果汁中苹果原汁含量的方法，具体包括以下步骤：The present invention is realized by the following scheme: a method for calculating the content of apple juice in blended apple juice by using near-infrared spectroscopy technology, specifically comprising the following steps:
步骤S1：制备含有不同含量苹果原汁的勾兑苹果汁作为样本，将样本进行近红外光谱扫描，采集其近红外光谱，并剔除异常的样本数据；Step S1: Prepare blended apple juice containing different contents of apple juice as a sample, scan the sample by near-infrared spectrum, collect its near-infrared spectrum, and eliminate abnormal sample data;
步骤S2：选取12000-4000cm-1特征波段下的光谱信息，运用多元散射校正预处理方法对所述光谱信息进行处理；经过散射校正后得到的光谱数据可以有效地消除散射影响，增强了与成分含量相关的光谱吸收信息；Step S2: Select the spectral information under the characteristic band of 12000-4000cm -1 , and use the multivariate scattering correction preprocessing method to process the spectral information; the spectral data obtained after scattering correction can effectively eliminate the influence of scattering, and enhance the Content-related spectral absorption information;
步骤S3：采用留一法交叉验证求得PLS最佳的因子数；Step S3: use leave-one-out cross-validation to obtain the optimal number of factors for PLS;
步骤S4：采用最佳的因子数，进行PLS建模，得到勾兑苹果汁中苹果原汁含量的预测模型；Step S4: Using the optimal number of factors, PLS modeling is carried out to obtain a prediction model for the content of apple juice in the blended apple juice;
步骤S5：对于未知的样品，扫描其近红外光谱，利用步骤S4建立好的勾兑苹果汁中苹果原汁含量的预测模型，预测未知勾兑苹果汁中的原汁含量；Step S5: For the unknown sample, scan its near-infrared spectrum, use the prediction model of the original apple juice content in the blended apple juice established in step S4, and predict the original juice content in the unknown blended apple juice;
进一步地，步骤S1中所述制备含有不同含量苹果原汁的勾兑苹果汁作为样本具体包括以下步骤：Further, the preparation of blended apple juice containing different contents of apple juice in step S1 as a sample specifically includes the following steps:
步骤S11：新鲜苹果汁的制备：取新鲜的苹果，经去皮、切碎、榨汁、过滤工艺得新鲜苹果汁，并测定其可溶性固形物含量；Step S11: Preparation of fresh apple juice: fresh apples are taken, peeled, chopped, squeezed, and filtered to obtain fresh apple juice, and the content of soluble solids is measured;
步骤S12：果汁粉冲剂的制备：用蒸馏水配置同样可溶性固形物含量的苹果汁粉；Step S12: Preparation of fruit juice powder granules: use distilled water to prepare apple juice powder with the same soluble solid content;
步骤S13：不同原汁含量的苹果汁制备：将具有相同可溶性固形物含量的新鲜苹果汁和果汁粉冲剂按照原汁比例为0～1进行勾兑，制作多个定标样本集。Step S13: Preparation of apple juices with different raw juice contents: Blend fresh apple juices with the same soluble solids content and juice powder granules according to the raw juice ratio of 0-1 to make multiple calibration sample sets.
进一步地，步骤S2中运用多元散射校正预处理方法对所述光谱信息进行处理具体为：首先建立一个待测样品的理想光谱，所述理想光谱即光谱的变化与样品中成分的含量满足直接的线性关系，以该理想光谱为标准要求对所有其他样品的近红外光谱进行修正；所述修正包括基线平移和偏移校正。Further, in step S2, using the multivariate scattering correction preprocessing method to process the spectral information is specifically as follows: firstly, an ideal spectrum of the sample to be measured is established, and the ideal spectrum, that is, the change of the spectrum and the content of the components in the sample meet the direct relationship A linear relationship, taking this ideal spectrum as a standard requires corrections to the near-infrared spectra of all other samples; said corrections include baseline shift and offset correction.
与现有技术相比，本发明有以下有益效果：本发明所采取的方法不仅能够快速测量勾兑苹果汁中苹果原汁含量，并且具有高精度、操作简单等优点，在后续的不同混合物中的原汁含量检测的工作中有重大贡献，具有良好的应用前景。Compared with the prior art, the present invention has the following beneficial effects: the method adopted in the present invention can not only quickly measure the content of the original apple juice in the blended apple juice, but also has the advantages of high precision and simple operation. It has made great contributions to the detection of raw juice content and has a good application prospect.
附图说明Description of drawings
图1为本发明实施例中所述勾兑果汁中不同含量的苹果原汁对应的近红外原始吸收光谱图。Fig. 1 is the near-infrared original absorption spectrum corresponding to different contents of apple juice in the blended fruit juice described in the embodiment of the present invention.
图2为本发明实施例中经过多元散射校正预处理后的勾兑果汁中不同含量的苹果原汁对应的近红外吸收光谱图。Fig. 2 is a near-infrared absorption spectrum corresponding to different contents of apple juice in the blended fruit juice after multivariate scattering correction pretreatment in the embodiment of the present invention.
图3为本发明实施例中利用留一法交叉验证取得最佳主成分因子数变化图。Fig. 3 is a change diagram of the number of optimal principal component factors obtained by leave-one-out cross-validation in an embodiment of the present invention.
图4为本发明实施例中PLS模型对勾兑苹果汁中原汁含量的预测值和实际值间的关系图。Fig. 4 is a graph showing the relationship between the predicted value and the actual value of the original juice content in the blended apple juice by the PLS model in the embodiment of the present invention.
具体实施方式detailed description
下面结合附图及实施例对本发明做进一步说明。The present invention will be further described below in conjunction with the accompanying drawings and embodiments.
本实施例提供了一种采用近红外光谱技术计算勾兑苹果汁中苹果原汁含量的方法，具体包括如下步骤：This embodiment provides a method for calculating the content of apple juice in blended apple juice using near-infrared spectroscopy, which specifically includes the following steps:
（1）新鲜苹果汁的制备：取新鲜的红富士苹果，经去皮、切碎、榨汁、过滤等工艺得新鲜苹果汁，并测定其可溶性固形物含量。果汁粉冲剂的制备：用蒸馏水配置同样可溶性固形物含量的苹果汁粉。不同原汁含量的苹果汁制备：将具有相同可溶性固形物含量的新鲜苹果汁和果汁粉冲剂按照原汁比例为0～1进行勾兑，制作21个定标样本集。(1) Preparation of fresh apple juice: Fresh red Fuji apples were taken, peeled, chopped, squeezed, filtered, etc. to obtain fresh apple juice, and the soluble solid content was determined. Preparation of fruit juice powder granule: configure apple juice powder with the same soluble solids content with distilled water. Preparation of apple juice with different raw juice content: Blend fresh apple juice and juice powder granules with the same soluble solid content according to the raw juice ratio of 0-1, and make 21 calibration sample sets.
使用2mm石英比色皿盛装待上述样品，实验所用的仪器为傅立叶转换近红外光谱分析仪Nicolet 6700 FT-NIR（美国Thermo Fisher）。以空气作为测量背景，空气湿度为60%，在室温25℃下测定，采集样品的透射近红外光谱。波数范围为12000-4000cm-1，分辨率为16，每个样品扫描3次，取其平均值作为该样品的近红外吸收光谱，如图1所示。数据采集使用Nicolet 6700 FT-NIR近红外光谱软件平台OMNICE软件，数据分析均在MATLAB R2016a中进行。A 2mm quartz cuvette was used to hold the above samples, and the instrument used in the experiment was a Fourier transform near-infrared spectrometer Nicolet 6700 FT-NIR (Thermo Fisher, USA). The air is used as the measurement background, the air humidity is 60%, and the measurement is carried out at a room temperature of 25°C, and the transmission near-infrared spectrum of the sample is collected. The wavenumber range is 12000-4000cm -1 , the resolution is 16, each sample is scanned 3 times, and the average value is taken as the near-infrared absorption spectrum of the sample, as shown in Fig. 1 . Data collection was performed using Nicolet 6700 FT-NIR near-infrared spectroscopy software platform OMNICE software, and data analysis was performed in MATLAB R2016a.
(2)对于异常的样本进行剔除，并运用多元散射校正预处理方法进行对光谱信息进行处理。经过散射校正后得到的光谱数据可以有效地消除散射影响，增强了与成分含量相关的光谱吸收信息。该方法的使用首先要求建立一个待测样品的“理想光谱”，即光谱的变化与样品中成分的含量满足直接的线性关系，以该光谱为标准要求对所有其他样品的近红外光谱进行修正，其中包括基线平移和偏移校正等，如图2。(2) Eliminate the abnormal samples, and use the multivariate scattering correction preprocessing method to process the spectral information. The spectral data obtained after scattering correction can effectively eliminate the scattering effect and enhance the spectral absorption information related to the component content. The use of this method first requires the establishment of an "ideal spectrum" of the sample to be tested, that is, the change of the spectrum meets the direct linear relationship with the content of the components in the sample, and the near-infrared spectrum of all other samples is required to be corrected based on this spectrum. These include baseline translation and offset correction, etc., as shown in Figure 2.
(3)将共21个样本利用KS算法将采集的样本分为校正集与预测集，其中以原汁比例为0、0.10、0.20、0.30、0.40、0.50、0.60、0.70、0.80、0.90、1.00的样本共11组数据作为校正集，用来优选模型参数和模型结果；以0.05、0.15、0.25、0.35、0.45、0.55、0.65、0.75、0.85、0.95样本共10组数据作为预测集，用来验证模型的有效性。(3) A total of 21 samples were divided into a calibration set and a prediction set using the KS algorithm, in which the ratio of the original juice was 0, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 1.00 A total of 11 sets of data from the sample are used as calibration sets to optimize model parameters and model results; a total of 10 sets of data from samples of 0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, and 0.95 are used as prediction sets to Verify the validity of the model.
(4)采用留一法交叉验证求的PLS最佳的因子数，如图3。(4) The optimal number of factors of PLS obtained by leave-one-out cross-validation, as shown in Figure 3.
(5)采用最佳的因子数10，进行PLS建模。对预测集样本运用建立好的PLS模型进行苹果原汁分析，模型的相关系数为0.9991，预测均方误差为0.0121，预测值与实际含量间的关系图如图4所示。(5) Use the best factor number of 10 to carry out PLS modeling. The established PLS model was used to analyze the original apple juice of the prediction set samples. The correlation coefficient of the model was 0.9991, and the predicted mean square error was 0.0121. The relationship between the predicted value and the actual content is shown in Figure 4.
以上所述仅为本发明的较佳实施例，凡依本发明申请专利范围所做的均等变化与修饰，皆应属本发明的涵盖范围。The above descriptions are only preferred embodiments of the present invention, and all equivalent changes and modifications made according to the scope of the patent application of the present invention shall fall within the scope of the present invention.
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