Fast Discrimination of Apple Varieties Using Vis/NIR Spectroscopy

We evaluated the potential of visible/near-infrared (Vis/NIR) spectroscopy for its ability to nondestructively differentiate apple varieties. The apple varieties used in this research included, Fuji apples, Red Delicious apples, and Copefrut Royal Gala apples. The chemometrics procedures applied to the Vis/NIR data were principal component analysis (PCA), wavelet transform (WT), and artificial neural network (ANN). The apple varieties could be qualitatively discriminated in the PC1-PC2 space resulted from PCA. Wavelet transform was used as a tool for dimension reduction and noise removal, reducing spectral to wavelet components. Wavelet components were utilized as input for three-layer back propagation ANN model. WT-ANN model gave the highest level of correct classification (100%) of the apple varieties.     

Nondestructive Measurement of Acidity of Strawberry Using Vis/NIR Spectroscopy

Vis/Near infrared reflectance spectroscopy appears to be a rapid and convenient non-destructive technique that can measure the quality and compositional attributes of many substances. This paper assesses the ability of NIR reflectance spectroscopy to estimate the acidity of strawberry. Spectra were collected from 65 samples and data was expressed as absorbance, the logarithm of the reciprocal of reflectance (log1/R). The absorbance data was subsequently compressed using wavelet transformation. Two models to predict the acidity in strawberry were constructed. A prediction model based on wavelet transform (WT) combined with partial least squares (PLS) was found better with the r of 0.856, RMSEP of 0.026, and in the confidence lever 95%.     

基于近红外光谱的大米加工精度等级快速判定

将30个粳稻3、6个籼稻品种糙米碾削20～95 s,得到不同糙出白率的白米。分别采用称重法和染色法测出每个样本的糙出白率和加工精度等级,用FT-NIR近红外光谱仪采集每个样本的近红外光谱,以偏最小二乘法建立稻谷糙出白率的定标模型并对模型进行验证,得到最小预测误差(RMSEP)为0.032 4%,相应的决定系数(R2)为0.999 5;对稻米加工精度等级与糙出白率关系进行统计分析,发现随着稻米加工精度等级的提高,糙出白率呈对数关系降低。基于预测得到的糙出白率,建立稻米加工精度等级判定模型,结果显示,对数模型的预测精度为82.5%,采用马氏距离判别法建立的模型预测精度可达98.33%。     

近红外光谱分析技术在制浆造纸中的应用

本文介绍了近红外(NIR)光谱分析的测定原理、技术特点以及应用现状,并着重介绍了其在制浆造纸领域的应用,如测定纸浆木素含量、快速测定原料成分、纸浆卡伯值的在线检测和其它浆纸性能的测定等,展示了近红外光谱分析技术在制浆造纸领域巨大的应用前景.     

Quality Assessment of Intact Chicken Breast Fillets Using Factor Analysis with Vis/NIR Spectroscopy

Factor analysis (FA) method was tested to assess quality of chicken breast fillets with the visible/near-infrared (Vis/NIR) spectroscopy with wavelength range between 400 and 2500 nm. According to inherent correlation, three factors were extracted from the measured eight quality traits (L*, a*, b*, pH, moisture, drip loss, expressible fluid, and salt-induced water gain). The extracted “grade factor” (F ₁), “color factor” (F ₂), and “moisture factor” (F ₃) could respectively represent the characteristics and the variation tendency of the corresponding quality traits and were defined as three new quality assessment indexes. Furthermore, partial least squares regression (PLSR) models were established to quantitatively relate spectral information to eight individual quality traits and three factors. The results indicated that the models for predicting each factor performed better than those for individual quality traits. Key wavelengths of each quality trait were then selected, and the corresponding spectra were taken to build new PLSR prediction models. The selected key wavelengths showed obvious practical significance, and the new models had comparable predictive performance to those models developed based on the full spectra, among which the new models of F ₁ and F ₂ had acceptable and robust predictive abilities (R²p?=?0.73, RPD?=?1.91; R²p?=?0.74, RPD?=?1.97). Our results in the present study demonstrate the potential for FA and Vis/NIR spectroscopy as a useful method to assess the quality of chicken breast fillets.     

基于近红外光谱快速定量检测面粉中曲酸的方法建立

利用近红外光谱技术快速定量检测面粉中非法添加的褐变抑制剂曲酸。选取市场上常见3?种基本类型的面粉（高、中、低筋面粉）,分别制备曲酸质量分数为0.0%、0.5%、1.0%、3.0%、5.0%、10.0%的面粉样品,并采集其在1?000～2?400?nm波段下的光谱数据。对比不同预处理下高筋面粉样品数据所建偏最小二乘（partial least squares,PLS）回归模型效果,选取Savitzky-Golay一阶导数为最优预处理方法。采用区间偏最小二乘（interval partial least squares,iPLS）法选取1?088.8～1?153.5?nm为最佳光谱区间。结果表明,基于最佳光谱区间所建PLS回归模型预测效果优于基于全波段光谱数据所建模型。进一步,基于所选最优区间对中、低筋面粉和混合样品集分别建立PLS回归模型。高、中、低筋面粉及混合样品集基于最优区间的PLS模型的决定系数为0.949～0.972,标准误差为0.581%～0.830%,验证集标准偏差与预测标准偏差的比值为4.171～4.830。结果表明,基于最优区间的近红外光谱方法对不同类型面粉中曲酸质量分数为1.0%～10.0%的样品具有较好的预测结果,结合具有低检测限的化学检测方法,在对大批量样品的检测中可提高检测效率。     

甲鱼饲料中红鱼粉的酸价改良

分别对红鱼粉进行直接加热、环模制粒和挤压膨化三种方式的水热处理,以降低红鱼粉的酸价.直接加热后酸价最低为1.62 mgKOH/g,其处理条件:含水量11.5%,加热温度120℃,加热时间40 min;环模制粒后酸价最低为1.52 mgKOH/g,其处理条件:入模水分为16.61%,调质温度85℃;挤压膨化后酸价最低为1.23 mgKOH/g,其处理条件;含水量24%,加热温度110℃.结果表明,挤压膨化后的红鱼粉能达到甲鱼饲料原料的酸价要求.     

近红外技术在卷烟真伪鉴别中的应用

为了快速准确地鉴别卷烟的真伪,对A牌卷烟进行了近红外光谱扫描,采用光谱因子分析法建立了A牌卷烟的鉴别模型,并应用该模型对A牌(样品数88个)、非A牌(样品数80个)和假冒A牌卷烟(样品数10个)进行了鉴别。结果显示,A牌、非A牌和假冒A牌卷烟的鉴别准确率分别为92.0%、96.3%和100.0%,平均94.4%。该模型鉴别的准确性较高,可以作为卷烟真伪鉴别的一种辅助手段。     

Simultaneous Classification of Teas According to Their Varieties and Geographical Origins by Using NIR Spectroscopy and SPA-LDA

Due to the human health benefits already scientifically proven, tea (Camellia sinensis) has been widely studied in the literature. Several studies report the classification of the variety or geographical origin of teas, separately. Thus, this paper has proposed a methodology for simultaneous classification of tea samples according to their varieties (green or black) and geographical origins (Brazil, Argentina, or Sri Lanka). For this purpose, near-infrared (NIR) spectroscopy and three differing supervised pattern recognition techniques, namely SIMCA (soft independent modeling of class analogy), PLS-DA (partial least squares-discriminant analysis), and SPA-LDA (successive projections algorithm associated with linear discriminant analysis) have been used. Despite having good results, both full-spectrum PLS-DA and SIMCA were not able to achieve 100 % classification accuracy, regardless of the significance level for the F test in the case of the SIMCA model. On the other hand, the resulting SPA-LDA model successfully classified all studied samples into five differing tea classes (Argentinean green tea; Brazilian green tea; Argentinean black tea; Brazilian black tea; and Sri Lankan black tea) using 12 wave numbers alone.     

Application of Vis/NIR Spectroscopy for Chinese Liquor Discrimination

As one of the most widely consumed alcoholic beverages, Chinese liquor varies greatly in price, flavor, and quality. This diversity calls for effective and reliable discrimination methods. In an attempt to find the best liquor discrimination method, this study used different methods to analyze and identify 730 Chinese liquor samples including 22 kinds, ten brands, and six flavors. These samples, covering most of the famous liquors in China, were analyzed by visible and near-infrared (Vis/NIR) spectroscopy and modeled by three classification methods including supporting vector machine, soft independent modeling of class analogy, and linear discriminate analysis based on principal component analysis (PCA-LDA). Pretreatments and parameters for each model were optimized, and models discrimination ability was compared. The research finds that PCA-LDA was the best model with an average prediction rate of 98.94 % in the training set and 95.70 % in the test set. The correct rates for brands, flavor styles, ages, and alcohol degrees were all higher than 95 %. It shows that Vis/NIR is a reliable, inexpensive, and effective tool for Chinese liquors discrimination.     

基于近红外光谱法的木材原料的Kohonen网络分类法

使用近红外光谱法结合Kohonen网络对不同的造纸木材原料进行了快速分类与评价.先对原料的近红外光谱数据进行小波变换预处理,再利用主成分分析法对小波变换预处理后的光谱数据进行信息的压缩和提取,选择了第一、二主成分可表达原有光谱数据信息,再把这2个主成分输入Kohonen网络进行分类处理,从处理结果来看可较好地区分马尾松、杨木和桉木等3种不同制浆性能的木材原料,且能较好表达木材原料类别之间的性质关系.     

高温环境下蛋鸡饲料的配制

通过对菲律宾长城家禽农场技术服务,介绍了高温季节蛋鸡饲料的配制技术及饲用效果。饲用结果表明,蛋鸡饲料中通过增加蛋白质含量、降低饲料能量、增加适量的维生素及抗应激剂等措施,可增加蛋鸡的采食量和抗热应激能力,产蛋率提高4．1％,平均蛋重增加2．76克,成本降低13％,死亡率有所降低,达到了预期效果。     

基于中波近红外光谱定量分析生鲜牛肉胆固醇

胆固醇是人体必需的一种具有重要生理功能的动物源性甾醇类物质,摄入不均衡会引发严重的心脑血管疾病。利用近红外光谱技术,在中波波段建立一种对动物源性胆固醇含量进行快速无损定量分析的新方法并验证其可行性。采集市售生鲜牛肉原始光谱,结合马氏距离、学生残差及二审剔除判别法甄别并剔除疑似异常样本,经光谱预处理和模型优化后,建立最佳牛肉胆固醇偏最小二乘法定量分析模型。结果表明,基于近红外光谱中波波段所构建的胆固醇定量分析模型对独立检验集的预测效果较好（P＞0.05）,整体预测准确率满足国标要求（精密度≤10%）。     

新型复合酶在肉鸡饲料中的应用

为了降低麦糟中的非淀粉多糖抗营养因子,提高饲料的转化率,促进家禽对营养物质的吸收,作者研究了溢多酶和阿魏酸酯酶在不同的温度、酶解时间、阿魏酸酯酶添加量、麦糟水分质量分数及酶的添加方式等条件下麦糟的降解效果,以及酶解后的麦糟对对肉鸡生产性能的影响。结果表明:酶解时间2.5 d,操作温度40℃,水分质量分数50%,添加阿魏酸酯酶酶量为6.6-8.8 U/g麦糟,阿魏酸酯酶与溢多酶同时添加对麦糟酶解效果最好。将按不同方式酶解后的麦糟以7%的比例添加入肉鸡日粮中,与不加酶组对比,同时添加阿魏酸酯酶及溢多酶的实验组日增重提高4.37%(P>0.05),料肉比降低7.63%(P<0.01),阿魏酸酯酶与溢多酶能够显著促进肉鸡的生长。     

On-Site NIR Spectroscopy to Control the Shelf Life of Pork Meat

Control of meat shelf-life includes the time that it remains in the exhibitor of sale (such as the supermarket) until its rejection for the consumer, or withdrawal due to expiry date. Near infrared spectroscopy (NIRS) is one of the most promising techniques for large-scale meat quality control. This study investigated the potential of on-site NIRS portable instrumentation-based models to predict three microbiological parameters to establish if pork meat is acceptable or not for consumption (aerobic Mesophilous microorganisms, Enterobacteriaceae, and lactic acid bacteria) and pH to quality control food preservation and shelf-life extension on intact slices of pork meat packaged under two different modified atmospheres. NIR calibrations were developed by using an on-site Phazir instrument (Polychromix, Wilmington, MA, USA) in the range 1,600–2,400 nm. A total of 252 samples of pork meat slices were directly scanned twice in reflectance mode on trays, once before and another one after removing the film cover at 1, 3, 5, 7, 9, 12, and 15 days of storage. Results showed that spectra of meat acceptable or not for consumption have marked differences around 1,660 nm. NIRS quantitative prediction models showed r ² values between 0.19 and 0.65 for the microbiological parameters assayed. The developed NIRS methodology makes possible on-site prediction of microbiological status of pork meat with a standard error of cross-validation around 1 log cfu/g. Results have shown that modified atmosphere packaging has no influence on calibration statistics.     

Quantification of Mold in Hay by near Infrared Reflectance Spectroscopy

Near infrared reflectance spectroscopy was used to quantify mold by spectrally predicting chitin, a fungal cell wall polymer of glucosamine. Chitin data and near infrared reflectance spectra were collected from moldy alfalfa (Medicago sativa L.) hay samples. Chitin concentrations ranged from 75 to 710 μg/g DM and were similar to concentrations in other contaminated products. Second derivative math transformations were made on the spectra, and a calibration equation was developed by regressing transformed spectra against chemical data. The coefficients of determination for calibration and validation were .90 and .82, respectively. Standard errors of calibration and validation were 44 and 65 μg/g DM, respectively. The calibration equation utilized four terms, represented at wavelengths 1630, 2114, 2246,and 2356 nm. The spectra of commercial glucosamine were plotted, and reflectance peaks occurred at 2114 and 2356 nm. The validated equation was used to predict mold (chitin) in bales that had been visually estimated for mold with a relative mold index. Relative mold index was regressed against chitin and was correlated to chitin (R=.86); residual error was small when samples were extremely moldy or clean. We concluded that near infrared reflectance spectroscopy can accurately quantify mold in alfalfa hay.