Simultaneous classification of multiple classes in NMR metabolomics and vibrational spectroscopy using interval-based classification methods: iECVA vs iPLS-DA

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

Discrimination of Grape Seeds Using Laser-Induced Breakdown Spectroscopy in Combination with Region Selection and Supervised Classification Methods

The wine-making industry generates a considerable amount of grape pomace. Grape seeds, as an important part of pomace, are rich in bioactive compounds and can be reutilized to produce useful derivatives. The nutritional properties of grape seeds are largely influenced by the cultivar, which calls for effective identification. In the present work, the spectral profiles of grape seeds belonging to three different cultivars were collected by laser-induced breakdown spectroscopy (LIBS). Three conventional supervised classification methods and a deep learning method, a one-dimensional convolutional neural network (CNN), were applied to establish discriminant models to explore the relationship between spectral responses and cultivar information. Interval partial least squares (iPLS) algorithm was successfully used to extract the spectral region (402.74–426.87 nm) relevant for elemental composition in grape seeds. By comparing the discriminant models based on the full spectra and the selected spectral regions, the CNN model based on the full spectra achieved the optimal overall performance, with classification accuracy of 100% and 96.7% for the calibration and prediction sets, respectively. This work demonstrated the reliability of LIBS as a rapid and accurate approach for identifying grape seeds and will assist in the utilization of certain genotypes with desirable nutritional properties essential for production rather than their being discarded as waste.

A novel critical control point and chemical marker identification method for the multi-step process control of herbal medicines via NMR spectroscopy and chemometrics

Herbal medicines have played a vital role in maintaining the health of the world population in the past thousands of years, and have proved to be an effective therapy. It is important to improve our understanding of the effects of the multi-step processing in herbal medicines on the chemical changes to ensure product quality. A proton nuclear paramagnetic resonance (¹H NMR)-based evaluation strategy was developed for an efficient process variation exploration and diversified metabolite identification. In this study, 48 process intermediates from 6 commercial batches of the multi-step manufacturing chain of Danshen processing were obtained. Hierarchical classification analysis (HCA) tree based on ¹H NMR spectra clustered the samples according to the processing steps, which indicates that ¹H NMR has the potential capability for critical control point identification based on its adequate information of the organic compounds. Then, principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were applied to distinguish the major metabolite differences between the intermediates before and after the critical control point. In this case, the alkali-isolation and acid-dissolution method was recognized as the most critical process in the multi-step chain of Danshen extract manufacturing. Potential metabolites with the larger amplitude of variation and contributing the most to the discrimination were found to be potential quality markers by S-plot, including several previously undetected amino acids. The results in this study are consistent with previous research studies and reference experiments conducted with other analytical tools. Taken together, they prove that ¹H NMR with chemometrics is a very effective process quality control tool to provide comprehensive information on the chemical changes during the processing of herbal medicines, and help with the identification of critical control points and potential critical quality markers.

NMR and chemometric-based critical control point and chemical marker identification for the multi-step process control of herbal medicines.