Differentiating five agrochemicals used in the treatment of intact olives by means of NIR spectroscopy, discriminant analysis and compliant class models

IF 4.9 2区 化学 Q1 CHEMISTRY, ANALYTICAL Microchemical Journal Pub Date : 2024-09-06 DOI:10.1016/j.microc.2024.111550
D. Castro-Reigía, I. García, S. Sanllorente, L.A. Sarabia, M.C. Ortiz
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Abstract

This paper deals with the application of near infrared spectroscopy (NIR) in a classification problem involving multiple classes in order to differentiate contaminated olives. A total of 452 samples, ripe and unripe, were treated with five different agrochemicals reproducing the traditional fumigation process in the olive tree. The main objective was to differentiate through a classification if the samples were or were not treated, but also, which chemical was used for each olive. Firstly, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to differentiate between untreated and treated samples. Then, two novel chemometric approaches, a classification one and a modelling one, were applied for ripe and unripe olives, achieving good results and determining with which chemical were the olives sprinkled with. For the classification of the samples in the six different classes (untreated olives, or treated with one of the five agrochemicals), an Automatic Hierarchical Model Builder (AHIMBU) was used, applying sequential binary PLS-DAs. Nevertheless, for the modelling approach, a compliant model, PLS2-CM, also based on PLS, was used with two different codifications for the classes: i) the classic and well-known One Versus All (OVA), and ii) the Error Correction Output Code (ECOC) optimal matrix. The final global results were evaluated using the Diagonal Modified Confusion Entropy (DMCEN) index, which ranges between 0 and 1, and is very sensitive to changes in the sensitivity–specificity matrices (note that the lower the DMCEN, the better the classification is). The best DMCEN value in prediction for unripe olives, 0.4898, was obtained for the PLS2-CM-ECOC, while 0.6937 and 0.7705 DMCEN values were obtained for AHIMBU and PLS2-CM-OVA, respectively. For the case of the ripe samples, the DMCEN values in prediction were better than the ones for the unripe olives: 0.6016, 0.5051, and 0.4166, for AHIMBU, PLS2-CM-OVA and PLS2-CM-ECOC, respectively. In every case, the best DMCEN has been obtained with the PLS2-CM-ECOC procedure.
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通过近红外光谱、判别分析和顺应类模型区分用于处理完整橄榄的五种农用化学品
本文探讨了近红外光谱(NIR)在涉及多个类别的分类问题中的应用,以区分受污染的橄榄。共对 452 个成熟和未成熟的橄榄样品使用了五种不同的农用化学品,再现了橄榄树的传统熏蒸过程。主要目的是通过分类来区分样本是否经过处理,以及每种橄榄使用了哪种化学品。首先,通过偏最小二乘法判别分析(PLS-DA)来区分未处理和处理过的样品。然后,对成熟和未成熟的橄榄采用了两种新的化学计量方法,一种是分类方法,另一种是建模方法,取得了良好的效果,并确定了橄榄使用的化学物质。在对六个不同类别(未处理过的橄榄或用五种农用化学品之一处理过的橄榄)的样品进行分类时,使用了自动分层模型生成器(AHIMBU),应用了连续的二元 PLS-DAs。不过,在建模方法上,使用了一个同样基于 PLS 的兼容模型 PLS2-CM,该模型有两种不同的分类编码方式:i) 经典且著名的 "一对全"(OVA),以及 ii) 纠错输出码(ECOC)最佳矩阵。最终的全局结果使用对角线修正混淆熵(DMCEN)指数进行评估,该指数介于 0 和 1 之间,对灵敏度-特异性矩阵的变化非常敏感(注意,DMCEN 越低,分类效果越好)。PLS2-CM-ECOC 预测未熟橄榄的最佳 DMCEN 值为 0.4898,而 AHIMBU 和 PLS2-CM-OVA 的 DMCEN 值分别为 0.6937 和 0.7705。就成熟样品而言,预测的 DMCEN 值优于未熟橄榄的 DMCEN 值:AHIMBU、PLS2-CM-OVA 和 PLS2-CM-ECOC 的 DMCEN 值分别为 0.6016、0.5051 和 0.4166。在所有情况下,PLS2-CM-ECOC 程序都获得了最佳的 DMCEN 值。
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来源期刊
Microchemical Journal
Microchemical Journal 化学-分析化学
CiteScore
8.70
自引率
8.30%
发文量
1131
审稿时长
1.9 months
期刊介绍: The Microchemical Journal is a peer reviewed journal devoted to all aspects and phases of analytical chemistry and chemical analysis. The Microchemical Journal publishes articles which are at the forefront of modern analytical chemistry and cover innovations in the techniques to the finest possible limits. This includes fundamental aspects, instrumentation, new developments, innovative and novel methods and applications including environmental and clinical field. Traditional classical analytical methods such as spectrophotometry and titrimetry as well as established instrumentation methods such as flame and graphite furnace atomic absorption spectrometry, gas chromatography, and modified glassy or carbon electrode electrochemical methods will be considered, provided they show significant improvements and novelty compared to the established methods.
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