基于近红外光谱技术的茶籽油掺伪定性鉴别和定量分析

茶籽油是我国特有的高级食用油。近年来茶籽油掺伪现象层出不穷,本研究利用傅里叶近红外(FT-NIR)光谱与化学计量学相结合,获得一种快速对不同掺假类型（低芥酸菜籽油、大豆油、二元混合油）和不同掺假度（0%-100%）的茶籽油进行定性和定量检测的方法。基于近红外差异光谱进行判别分析（discriminant analysis,DA）,DA成功识别了不同掺假度的二元与三元混合茶籽油。通过对手动与自动筛选的不同波段结合不同预处理方法建立偏最小二乘法（Partial least squares,PLS）定量分析模型,最佳模型对掺假水平具有良好的预测性能,决定系数(R2)均大于0.91。校正均方根误差(RMSEC)和预测均方根误差(RMSEP)均接近于0。通过交叉验证,最佳模型的交叉验证相关系数均大于0.98,交叉验证均方根误差均小于0.05,表明筛选的最佳模型均具有良好的稳定性。通过外部验证,最佳模型对不同掺假类型的中、高掺假度（≥10%）的样品识别率高达100%。研究表明,利用近红外光谱技术结合化学计量学能够实现茶籽油掺假的快速、无损鉴定。     

Quantitative determination of fatty acid compositions in micro-encapsulated fish-oil supplements using Fourier transform infrared (FTIR) spectroscopy

The research describes a rapid method for the determination of fatty acid (FA) contents in a micro-encapsulated fish-oil (μEFO) supplement by using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopic technique and partial least square regression (PLSR) analysis. Using the ATR-FTIR technique, the μEFO powder samples can be directly analysed without any pre-treatment required, and our developed PLSR strategic approach based on the acquired spectral data led to production of a good linear calibration with R(2)=0.99. In addition, the subsequent predictions acquired from an independent validation set for the target FA compositions (i.e., total oil, total omega-3 fatty acids, EPA and DHA) were highly accurate when compared to the actual values obtained from standard GC-based technique, with plots between predicted versus actual values resulting in excellent linear fitting (R(2)≥0.96) in all cases. The study therefore demonstrated not only the substantial advantage of the ATR-FTIR technique in terms of rapidness and cost effectiveness, but also its potential application as a rapid, potentially automated, online monitoring technique for the routine analysis of FA composition in industrial processes when used together with the multivariate data analysis modelling.     

Predicting of intramuscular fat content in pork using near infrared spectroscopy and multivariate analysis

The potential of near infrared (NIR) spectroscopy combined with chemometrics methods was studied to rapidly detect intramuscular fat (IMF) content in pork. Near infrared diffuse reflectance spectra were recorded both with an FT-NIR and a USB4000 spectrometer. The data analysis was compared on different sample preparation, spectral range and spectra pretreatment. According to calibration statistics, best calibration for IMF showed R²_cal of 0.94, R²_val of 0.92, RMSEC of 0.233, RMSEP of 0.462 and RPD of 2.29. The prediction of IMF content for minced samples was more accurate than that for intact samples. The spectra obtained using FT-NIR contained much information correlating to the IMF content than the Vis-NIR spectra of USB4000. The results showed that NIR spectroscopy technique can be used to determine the IMF content in pork as a rapid, convenient, and feasible analysis tool.     

Inactivation of A. ochraceus spores and detoxification of ochratoxin A in coffee beans by gamma irradiation

Ochratoxin A (OTA) produced in food by Aspergillus ochraceus is known to cause adverse health effects. Among the plantation products, green coffee beans are prone to fungal attack and get contaminated with OTA frequently. A fungal strain isolated from green coffee beans was characterized by morphological analyses as well as internal transcribed spacer (ITS) and 5.8S rDNA sequencing, turned out to be A. ochraceus, however, nontoxigenic. Hence, additional strains of A. ochraceus were procured and characterized for toxin production. Presterilized green coffee beans were spiked with a toxigenic strain and treated with gamma radiation. Minimum inhibitory dose (MID) of gamma radiation for 10(4) and 10(8) spores of A. ochraceus strain per 10 g of green coffee beans was found to be approximately 1 and approximately 2.5 kGy, respectively. The radiation treatment (10 kGy) almost degraded the preformed or in vitro added OTA (50 ppb) in coffee beans. OTA degradation was found to be enhanced with increase in moisture content. Cytotoxicity in terms of cell viability was found to be reduced significantly for radiation treated OTA in MTT [3-(4,5-dimethylthiazole-2yl)-2,5-diphenyl tetrazolium bromide] assay as well as flow cytometric analysis when studied using human intestinal epithelial (Int-407) cells. Similar finding was also observed with E. coli MG1655 cells. Thus the inclusion of gamma radiation treatment in the postharvest processing chain of green coffee beans could help in eliminating toxigenic fungi as well as destroying preformed OTA without affecting the sensory attributes. PRACTICAL APPLICATION: In general, mycotoxins including ochratoxin A (OTA) are highly stable to detoxifying agents. Green coffee beans are prone to fungal attack and could get frequently contaminated with the OTA due to improper drying or rehydration during storage. Gamma radiation processing of green coffee beans was found to eliminate the A. ochraceus spores as well as inactivate OTA without affecting its sensory attributes. Thus inclusion of gamma radiation in the postharvest processing chain of green coffee beans would be very useful for consumer safety and coffee trade.     

短波近红外光谱-偏最小二乘法测定白酒中乙醇含量

应用近红外光谱技术结合偏最小二乘法（NIRS-PLS）建立白酒中乙醇含量定量分析数学模型。所建校正模型相关系数（Corr.Coeff.）达到0.99986,校正集均方根误差（RMSEC）为0.00225,预测均方根误差（RMSEP）为0.00137,模型通过交互验证检验,得出PLS因子数为4时预测残差平方和（PRESS）和交互验证均方根误差（RMSECV）最小。用所建模型测定样品与气相色谱分析结果相对误差不大于0.81%。实验结果表明该方法准确性、稳定性好、精密度高。      

在线近红外光谱在保健酒酒精度检测上的应用

采用短波近红外光谱仪器在线检测保健酒调配液生产线上产品的酒精度。通过使用一阶倒数(First derivative,FD)和平滑处理(Norris derivative filter,ND),对近红外图谱进行预处理,使用偏最小二乘法(Partial least square,PLS)建立了酒精度检测近红外模型。模型的校正集均方根误差(Root mean square error of calibration,RMSEC)为0.737,交互验证相关系数为0.9189;预测集均方根误差(Root mean square error of prediction,RMSEP)为0.788,交互验证相关系数为0.9425。实验数据显示,近红外计算酒精度数值与标准法测量数值相对偏差主要集中在±2%之间,该方法可以满足生产过程中在线检测酒精度的要求。      

Application of Fourier transform near-infrared spectroscopy to optimization of green tea steaming process conditions

In this study, we constructed prediction models by metabolic fingerprinting of fresh green tea leaves using Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares (PLS) regression analysis to objectively optimize of the steaming process conditions in green tea manufacture. The steaming process is the most important step for manufacturing high quality green tea products. However, the parameter setting of the steamer is currently determined subjectively by the manufacturer. Therefore, a simple and robust system that can be used to objectively set the steaming process parameters is necessary. We focused on FT-NIR spectroscopy because of its simple operation, quick measurement, and low running costs. After removal of noise in the spectral data by principal component analysis (PCA), PLS regression analysis was performed using spectral information as independent variables, and the steaming parameters set by experienced manufacturers as dependent variables. The prediction models were successfully constructed with satisfactory accuracy. Moreover, the results of the demonstrated experiment suggested that the green tea steaming process parameters could be predicted on a larger manufacturing scale. This technique will contribute to improvement of the quality and productivity of green tea because it can objectively optimize the complicated green tea steaming process and will be suitable for practical use in green tea manufacture.     

Analysis of potential lard adulteration in chocolate and chocolate products using Fourier transform infrared spectroscopy

Fourier transform infrared (FTIR) spectroscopy, in combination with attenuated total reflectance (ATR) and partial least square (PLS) regression, was used to detect the presence of lard in chocolate formulation. The spectral bands associated with lard, cocoa butter and their blends (ranging from 0% to 15% of lard in cocoa butter) were recorded, interpreted and identified. A semi-quantitative approach is proposed to measure the percent of lard in blends on the basis of spectral data at the frequency region 4000–650 cm⁻¹, using the equation y = 0.9225x + 0.5539. The coefficient of determination (R²) was 0.9872 with a standard error (SE) of 1.305. In this paper, the potential of FTIR spectroscopy as a rapid analytical tool for the quantitative determination of adulterants especially lard, in chocolate, is demonstrated.     

Analysis of pork adulteration in beef meatball using Fourier transform infrared (FTIR) spectroscopy

Meatball is one of the favorite foods in Indonesia. The adulteration of pork in beef meatball is frequently occurring. This study was aimed to develop a fast and non destructive technique for the detection and quantification of pork in beef meatball using Fourier transform infrared (FTIR) spectroscopy and partial least square (PLS) calibration. The spectral bands associated with pork fat (PF), beef fat (BF), and their mixtures in meatball formulation were scanned, interpreted, and identified by relating them to those spectroscopically representative to pure PF and BF. For quantitative analysis, PLS regression was used to develop a calibration model at the selected fingerprint regions of 1200-1000 cm(-1). The equation obtained for the relationship between actual PF value and FTIR predicted values in PLS calibration model was y = 0.999x + 0.004, with coefficient of determination (R(2)) and root mean square error of calibration are 0.999 and 0.442, respectively. The PLS calibration model was subsequently used for the prediction of independent samples using laboratory made meatball samples containing the mixtures of BF and PF. Using 4 principal components, root mean square error of prediction is 0.742. The results showed that FTIR spectroscopy can be used for the detection and quantification of pork in beef meatball formulation for Halal verification purposes.     

基于近红外高光谱成像技术的新鲜与冻融牛肉鉴别技术研究

研究运用近红外高光谱成像技术对新鲜与冻融的牛肉进行判别。将45个牛肉样品随机分为两组,第一组25个为新鲜样品,第二组20个作为冻融样品。本实验通过高光谱成像仪获取样品的光谱图像数据,并对图像校正处理后进行分割,分离出感兴趣区域(ROI,Region of Interest)。然后再提取感兴趣区域的平均光谱,并将其作为样品的高光谱数据。经过对高光谱数据的多元散射校正(MSC,Multiplicative Scatter Correction)预处理,应用偏最小二乘回归(PLSR,Partial Least Squares Regression),在全光谱范围(950~1500 nm)构建了本实验的最优模型。实验表明,该模型具有较高的预测精度,其判别正确率为94.4%。因此,近红外高光谱成像技术对冻融牛肉的鉴别检测具有适用性。      

Classification of Pecorino cheeses produced in Italy according to their ripening time and manufacturing technique using Fourier transform infrared spectroscopy

Fourier-transform infrared spectroscopy, followed by linear discriminant analysis of the spectral data, was used to classify Italian Pecorino cheeses according to their ripening time and manufacturing technique. The Fourier transform infrared spectra of the cheeses were divided into 18 regions and the normalized absorbance peak areas within these regions were used as predictors. Linear discriminant analysis models were constructed to classify Pecorino cheeses according to different ripening stages (hard and semi-hard) or according to their manufacturing technique (fossa and nonfossa cheeses). An excellent resolution was achieved according to both ripening time and manufacturing technique. Also, a final linear discriminant analysis model considering the 3 categories (hard nonfossa, hard fossa, and semi-hard nonfossa) was constructed. A good resolution among the 3 categories was obtained.     

Discrimination between immature and mature green coffees by attenuated total reflectance and diffuse reflectance Fourier transform infrared spectroscopy

The objective of this work was to evaluate the potential of Fourier transform infrared spectroscopy (FTIR) in the characterization and discrimination between immature and mature or ripe coffee beans. Arabica coffee beans were submitted to FTIR analysis by reflectance readings employing attenuated total reflectance (ATR) and diffuse reflectance (DR) accessories. The obtained spectra were similar, but in general higher absorbance values were observed for nondefective beans in comparison to immature ones. Multivariate statistical analysis (principal component analysis, PCA, and agglomerative hierarchical clustering, AHC) was performed in order to verify the possibility of discrimination between immature and mature coffee samples. A clear separation between immature and mature coffees was observed based on AHC and PCA analyses of the normalized spectra obtained by employing both ATR and DR accessories. Linear discriminant analysis was employed for developing classification models, with recognition and prediction abilities of 100%. Such results showed that FTIR analysis presents potential for the development of a simple routine methodology for separation of immature and mature coffee beans. Practical Application: The ultimate goal of this research is to be able to propose improvements in the way immature coffee beans are separated from graded mature beans in coffee facilities (cooperatives and other coffee producer's associations). The results obtained herein point toward FTIR as a potential tool for the aimed improvements.     

Authentication of Olive Oil Adulterated with Vegetable Oils Using Fourier Transform Infrared Spectroscopy

Determination of the authenticity of extra virgin olive oils has become more important in recent years following some infamous adulteration and contamination scandals. The study focused on application of Fourier transform infrared spectroscopy to identify the adulteration of olive oils. Single-bounce attenuated total reflectance measurements were made on pure olive oil and olive oil samples adulterated with varying concentrations of sunflower oil (20-100 mL vegetable oil/L of olive oil). Discriminant analysis using 12 principal components was able to classify the samples as pure and adulterated olive oils based on their spectra. A partial least squares model was developed and used to verify the concentrations of the adulterant. Furthermore, the discriminant analysis method was used to classify olive oil samples as distinct from other vegetable oils based on their infrared spectra.     

基于近红外光谱技术的大米品质分析与种类鉴别

应用近红外光谱(NIRS)技术建立了大米食味品质分析与种类快速鉴别的方法。提取了102份粉碎后大米样品的近红外光谱,采用偏最小二乘法(PLS)建立了大米水分、蛋白质和直链淀粉定量分析模型,对模型预测结果的准确性进行了评价。预测模型的内部交叉验证决定系数(R2)分别为:0.992、0.9792和0.9736;内部交叉验证标准差(RMSECV)分别为:0.141、0.201和0.209;模型外部验证决定系数(R2)分别为0.9861、0.912和0.9373;外部验证标准差(RMSEP)分别为0.179、0.206和0.243。通过计算样品的近红外光谱图之间的欧氏距离来反映不同样品间的差异,对不同属性和不同产地的大米进行了定性聚类分析,种类识别准确率达到100%。结果表明,NIRS分析技术可以用于对大米品质和种类的快速无损检测。     

面粉湿面筋近红外定量分析中的光谱预处理方法研究

采用10种不同的预处理方法对面粉湿面筋含量的近红外光谱数据进行预处理,并比较了10种预处理方法对偏最小二乘法（PLS）建立定量模型效果的影响。结果表明,采用近红外光谱（NIRS）分析技术对面粉中的湿面筋含量进行定量分析时,一阶导数+减去一条直线的预处理方法对面粉湿面筋含量的预测效果最好,内部交叉验证相关系数R为0.901 8,内部交叉验证标准差（RMSECV）为0.708;预测相关系数R为0.920 9,预测标准差（RMSEP）为0.083,提高了预测模型的精度和准确性。     

Comparison of fourier transform mid infrared spectroscopy and near infrared reflectance spectroscopy with differential scanning calorimetry for the study of the staling of bread

The staling of bread has previously been studied by differential scanning calorimetry and by simple rheological measurements. In this collaborative study, two spectroscopic techniques (Fourier transform mid infrared spectroscopy and near infrared reflectance spectroscopy) have been used in addition to differential scanning calorimetry to follow the progress of bread staling. Using each technique, changes in measured properties were apparent which, when fitted by first order exponential equations, gave calculated rate constants of similar magnitude. It is postulated that each technique gives independent information about the crystallisation process in the amylopectin fraction of the bread crumb.