Dalal Nidhi, Ofano Raffaela, Ruggiero Luigi, Caporale Antonio Giandonato, Adamo Paola
Department of Agricultural Sciences, University of Naples 'Federico II', Italy.
Curr Res Food Sci. 2024 Jun 17;9:100789. doi: 10.1016/j.crfs.2024.100789. eCollection 2024.
Food authentication is a growing concern with rising complexities of the food supply network, with fish being an easy target of food fraud. In this regard, NIR spectroscopy has been used as an efficient tool for food authentication. This article reviews the latest research advances on NIR based fish authentication. The process from sampling/sample preparation to data analysis has been covered. Special attention was given to NIR spectra pre-processing and its unsupervised and supervised analysis. Sampling is an important aspect of traceability study and samples chosen ought to be a true representative of the population. NIR spectra acquired is often laden with overlapping bands, scattering and highly multicollinear. It needs adequate pre-processing to remove all undesirable features. The pre-processing technique can make or break a model and thus need a trial-and-error approach to find the best fit. As for spectral analysis and modelling, multicollinear nature of NIR spectra demands unsupervised analysis (PCA) to compact the features before application of supervised multivariate techniques such as LDA, PLS-DA, QDA etc. Machine learning approach of modelling has shown promising result in food authentication modelling and negates the need for unsupervised analysis before modelling.
随着食品供应网络的复杂性不断增加，食品认证日益受到关注，鱼类成为食品欺诈的一个容易目标。在这方面，近红外光谱已被用作食品认证的有效工具。本文综述了基于近红外的鱼类认证的最新研究进展。涵盖了从采样/样品制备到数据分析的过程。特别关注了近红外光谱预处理及其无监督和有监督分析。采样是可追溯性研究的一个重要方面，所选择的样品应该是总体的真实代表。采集的近红外光谱通常带有重叠谱带、散射且高度多重共线性。它需要进行充分的预处理以去除所有不良特征。预处理技术可以决定一个模型的成败，因此需要采用反复试验的方法来找到最佳拟合。至于光谱分析和建模，近红外光谱的多重共线性性质要求在应用诸如线性判别分析（LDA）、偏最小二乘判别分析（PLS-DA）、二次判别分析（QDA）等有监督多元技术之前，先进行无监督分析（主成分分析（PCA））以压缩特征。机器学习建模方法在食品认证建模中已显示出有前景的结果，并且无需在建模前进行无监督分析。
