Addressing adulteration challenges of dried oregano leaves by NIR HyperSpectral Imaging

IF 3.7 2区 化学 Q2 AUTOMATION & CONTROL SYSTEMS
Veronica Ferrari , Rosalba Calvini , Camilla Menozzi , Alessandro Ulrici , Marco Bragolusi , Roberto Piro , Alessandra Tata , Michele Suman , Giorgia Foca
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引用次数: 0

Abstract

Dried oregano leaves are particularly prone to adulteration because of their widespread distribution and their easy mixing with leaves of other plants of lower commercial value, such as olive, myrtle, strawberry tree, or sumac. To reveal the presence of adulteration, in this study we considered an untargeted analytical approach, which instead of involving the a priori selection of specific compounds of interest is focused on defining the characteristic spectral signature of authentic oregano with respect to its most frequent adulterants. NIR HyperSpectral Imaging (NIR-HSI) represents a state-of-the-art, rapid and non-destructive technique, allowing for the collection of both spectral and spatial information from the sample, making it particularly suitable for characterizing visually heterogeneous samples.

Authentication issues are typically assessed through class modelling techniques and Soft Independent Modelling of class Analogy (SIMCA) is one of the most used algorithms in this scenario. However, the high variability and heterogeneity within the authentic oregano class resulted in poor outcomes when SIMCA was applied. As an alternative, Soft Partial Least Squares Discriminant Analysis (Soft PLS-DA) algorithm was applied to differentiate authentic oregano samples from pure adulterants. Soft PLS-DA represents a hybrid approach that combines the advantages of both discriminant and class modelling techniques. The resultant classification model has indeed led to promising results, achieving a prediction efficiency of 92.9 %. Finally, based on the percentage of pixels predicted as oregano in the Soft-PLSDA prediction images, a threshold value of 10 % was established, serving as a detection limit of NIR-HSI to distinguish authentic oregano samples from adulterated ones.

利用近红外超光谱成像技术解决牛至干叶掺假问题
牛至干叶特别容易掺假,因为它们分布广泛,很容易与其他商业价值较低的植物(如橄榄、桃金娘、草莓树或苏木)的叶子混在一起。为了揭示掺假现象的存在,我们在本研究中采用了一种非靶向分析方法,这种方法不涉及先验地选择特定的相关化合物,而是侧重于确定真品牛至与最常见掺假物的光谱特征。近红外超光谱成像(NIR-HSI)是一种先进、快速和非破坏性的技术,可以收集样品的光谱和空间信息,因此特别适用于描述视觉异质样品的特征。然而,在应用 SIMCA 时,真品牛至类别内的高变异性和异质性导致结果不佳。作为替代方案,我们采用了软偏最小二乘法判别分析(Soft PLS-DA)算法来区分牛至真品和纯掺假品。软偏最小二乘判别分析是一种混合方法,结合了判别技术和类别建模技术的优点。由此产生的分类模型确实取得了可喜的成果,预测效率达到 92.9%。最后,根据软 PLS-DA 预测图像中被预测为牛至的像素百分比,确定了 10% 的阈值,作为近红外-高光谱仪的检测限,以区分真假牛至样品。
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来源期刊
CiteScore
7.50
自引率
7.70%
发文量
169
审稿时长
3.4 months
期刊介绍: Chemometrics and Intelligent Laboratory Systems publishes original research papers, short communications, reviews, tutorials and Original Software Publications reporting on development of novel statistical, mathematical, or computer techniques in Chemistry and related disciplines. Chemometrics is the chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analysing chemical data. The journal deals with the following topics: 1) Development of new statistical, mathematical and chemometrical methods for Chemistry and related fields (Environmental Chemistry, Biochemistry, Toxicology, System Biology, -Omics, etc.) 2) Novel applications of chemometrics to all branches of Chemistry and related fields (typical domains of interest are: process data analysis, experimental design, data mining, signal processing, supervised modelling, decision making, robust statistics, mixture analysis, multivariate calibration etc.) Routine applications of established chemometrical techniques will not be considered. 3) Development of new software that provides novel tools or truly advances the use of chemometrical methods. 4) Well characterized data sets to test performance for the new methods and software. The journal complies with International Committee of Medical Journal Editors'' Uniform requirements for manuscripts.
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