Bala Manju, Sethi Swati, Sharma Sanjula, Mridula D, Kaur Gurpreet
Food Grains and Oilseeds Processing Division, ICAR - Central Institute of Post-Harvest Engineering and Technology, Ludhiana, India.
Department of plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India.
J Sci Food Agric. 2023 Feb;103(3):1294-1302. doi: 10.1002/jsfa.12223. Epub 2022 Oct 7.
In order to obtain more economic gains, some food products are adulterated with low-cost substances, if they are toxic, they may pose public health risks. This has called forth the development of quick and non-destructive methods for detection of adulterants in food. Near-infrared reflectance spectroscopy (NIRS) has become a promising tool to detect adulteration in various commodities. We have developed rapid NIRS based analytical methods for quantification of two cheap adulterants (grass pea and pea flour) in a popular Indian food material, chickpea flour.
The NIRS spectra of pure chickpea, pure grass pea, pure pea flour and adulterated samples of chickpea flour with grass pea and pea flour (1-90%) (w/w) were acquired and preprocessed. Calibration models were built based on modified partial least squares regression (MPLSR), partial least squares (PLS), principal component regression (PCR) methods. Based on lowest values of standard error of calibration (SEC) and standard error of cross-validation (SECV), MPLSR-NIRS models were selected. These models exhibited coefficient of determination (R ) of 0.999, 0.999, SEC of 0.905, 0.827 and SECV of 1.473, 1.491 for grass pea and pea, respectively. External validation revealed R and standard error of prediction (SEP) of 0.999 and 1.184, 0.997 and 1.893 for grass pea and pea flour, respectively.
The statistics confirmed that our MPLSR-NIRS based methods are quite robust and applicable to detect grass pea and pea flour adulterants in chickpea flour samples and have potential for use in detecting food fraud. © 2022 Society of Chemical Industry.
为了获取更多经济利益，一些食品会被掺入低成本物质，如果这些物质有毒，可能会对公众健康构成风险。这促使人们开发快速、无损的食品掺假检测方法。近红外反射光谱法（NIRS）已成为检测各种商品掺假的一种有前景的工具。我们已经开发出基于快速近红外反射光谱法的分析方法，用于定量检测一种受欢迎的印度食品原料鹰嘴豆粉中的两种廉价掺假物（草豌豆和豌豆粉）。
采集并预处理了纯鹰嘴豆、纯草豌豆、纯豌豆粉以及分别掺入1%-90%（w/w）草豌豆和豌豆粉的鹰嘴豆粉掺假样品的近红外反射光谱。基于改进的偏最小二乘回归（MPLSR）、偏最小二乘法（PLS）、主成分回归（PCR）方法建立了校准模型。基于校准标准误差（SEC）和交叉验证标准误差（SECV）的最低值，选择了MPLSR-NIRS模型。这些模型对草豌豆和豌豆的决定系数（R²）分别为0.999、0.999，SEC分别为0.905、0.827，SECV分别为1.473、1.491。外部验证显示，草豌豆和豌豆粉的R²和预测标准误差（SEP）分别为0.999和1.184、0.997和1.893。
统计数据证实，我们基于MPLSR-NIRS的方法相当稳健，适用于检测鹰嘴豆粉样品中的草豌豆和豌豆粉掺假物，并且有潜力用于检测食品欺诈。© 2022化学工业协会。
