Rapid detection of grape syrup adulteration with an array of metal oxide sensors and chemometrics

Q2 Engineering
Mahdi Ghasemi-Varnamkhasti , Puneet Mishra , Morteza Ahmadpour-Samani , Mojtaba Naderi-Boldaji , Davoud Ghanbarian , Mojtaba Tohidi , Zahra Izadi
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引用次数: 9

Abstract

Among the different cases of emerging food fraud during the post-harvest processing, the adulteration in grape syrup is one. Typically, the grape syrup is adulterated with some illegitimate foreign materials such as grape paste (sauce), date syrup and even adding sugar-water solution to the pure grape syrup. The present study deals with assessing an electronic nose (e-nose) consisting of eight different metal oxide semiconductor (MOS) sensors for prompt detection of adulteration in the grape syrup. Three different adulterants i.e. grape paste, date syrup and sugar-water solution, each at three levels of 50, 60 and 75%, were tested. The collected data from MOS were normalised and visualised with the help of standard normal variate (SNV) and principal component analysis (PCA), respectively. Moreover, the scores obtained from PCA were used to perform hierarchal cluster analysis (HCA) to identify the similarities between different adulterated mixtures and pure grape syrup. Three different classification cases were considered to (i) address the presence of adulteration, (ii) detect the different adulterants and (iii) classify the amount of each adulteration. Linear discriminant analysis (LDA) and multi-class support vector machine (SVM) were used for classification analyses. Results showed that PCA identified provided separate clusters for the MOS data corresponding to different adulterants and their levels. The HCA showed a hierarchal of similarities between pure grape syrup and different levels of adulterations. LDA and SVM resulted in a successful classification modelling. However, the performance of SVM was considerably better than LDA with classification accuracies of 98.6 ± 0.10%, 98.9 ± 1.16% and 95.1 ± 1.39% for detecting adulteration, different adulterants and different concentrations of adulterants, respectively. MOS sensors coupled with chemometrics could provide a useful instrument and fast procedure for detection of adulteration in grape syrup.

用一系列金属氧化物传感器和化学计量学快速检测葡萄糖浆掺假
在收获后加工过程中出现的各种食品欺诈案件中,葡萄糖浆掺假是其中之一。通常,葡萄糖浆中掺入了一些不合法的外来物质,如葡萄膏(酱)、枣糖浆,甚至在纯葡萄糖浆中加入糖水溶液。本研究涉及评估电子鼻(电子鼻)由八种不同的金属氧化物半导体(MOS)传感器组成,用于及时检测葡萄糖浆中的掺假。测试了三种不同的掺假物质,即葡萄膏、枣糖浆和糖水溶液,每种掺假物质的含量分别为50%、60%和75%。采用标准正态变量(SNV)和主成分分析(PCA)对采集的MOS数据进行归一化和可视化处理。此外,从PCA中获得的分数被用于进行层次聚类分析(HCA),以确定不同掺假混合物与纯葡萄糖浆之间的相似性。考虑了三种不同的分类情况,以(i)处理掺假的存在,(ii)检测不同的掺假剂,(iii)对每种掺假的数量进行分类。采用线性判别分析(LDA)和多类支持向量机(SVM)进行分类分析。结果表明,PCA识别为不同掺假物及其含量对应的MOS数据提供了单独的聚类。HCA显示了纯葡萄糖浆和不同掺假水平之间的相似性等级。LDA和SVM成功地建立了分类模型。但SVM在检测掺假、不同掺假和不同掺假浓度时的分类准确率分别为98.6 ± 0.10%、98.9 ± 1.16%和95.1 ± 1.39%,明显优于LDA。MOS传感器与化学计量学相结合,为葡萄糖浆中掺假的检测提供了一种有效的仪器和快速的方法。
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来源期刊
Engineering in Agriculture, Environment and Food
Engineering in Agriculture, Environment and Food Engineering-Industrial and Manufacturing Engineering
CiteScore
1.00
自引率
0.00%
发文量
4
期刊介绍: Engineering in Agriculture, Environment and Food (EAEF) is devoted to the advancement and dissemination of scientific and technical knowledge concerning agricultural machinery, tillage, terramechanics, precision farming, agricultural instrumentation, sensors, bio-robotics, systems automation, processing of agricultural products and foods, quality evaluation and food safety, waste treatment and management, environmental control, energy utilization agricultural systems engineering, bio-informatics, computer simulation, computational mechanics, farm work systems and mechanized cropping. It is an international English E-journal published and distributed by the Asian Agricultural and Biological Engineering Association (AABEA). Authors should submit the manuscript file written by MS Word through a web site. The manuscript must be approved by the author''s organization prior to submission if required. Contact the societies which you belong to, if you have any question on manuscript submission or on the Journal EAEF.
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