Qualitative classification of Dendrobium huoshanense (Feng dou) using fast non-destructive hand-held near infrared spectroscopy

Abstract

Because of the similar appearance and properties of different quality grades of the product, super Dendrobium huoshanense could be easily adulterated with first-grade D. huoshanense and second-grade D. huoshanense products, thereby affecting its clinical application and causing market distortion. In this study, a combination of hand-held near infrared spectroscopy and chemometrics was used to classify different grades of D. huoshanense. The standard normal variate was employed to preprocess the original near infrared spectra, following which linear analysis models (principal component analysis (PCA), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), and a non-linear support vector machine (SVM) model, were utilized to establish the identification models. The results showed that PCA analysis could not identify the three grades of D. huoshanense, and the LDA analysis could distinguish the second-grade from the other two grades. The PLSDA model resulted in prediction accuracies for the calibration cross-validation, and test sets of 91.83%, 83.58%, and 84.29%, respectively. Unfortunately, the super and first-grade D. huoshanense were not identified by the linear analysis model. Further analysis was performed with a non-linear model, where SVM was used to analyze all grades of D. huoshanense. The recognition rate of thel training set and validation set were 88% and 84%, respectively. All in all, the use of a hand-held near infrared spectrometer combined with chemometrics could identify the quality grade of D. huoshanense samples on-site in real-time, and provide a simple, fast, and reliable method for the quality control of the traditional Chinese medicine herb of D. huoshanense.

The Application of Handheld Near-Infrared Spectroscopy and Raman Spectroscopic Imaging for the Identification and Quality Control of Food Products

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Comparative analysis of data preprocessing methods and machine learning models for geographical origin prediction in an imbalanced Panax notoginseng dataset using near-infrared spectroscopy

“…The rami cations of imbalanced sample distribution on geographical origin identi cation within P. notoginseng datasets are profound, signi cantly impacting the performance of machine learning models [39][40][41][42][43][44][45][46][47][48][49][50][51][52] . Biased model training in favor of the majority class jeopardizes sensitivity to minority classes, hindering the models' ability to generalize to underrepresented origins.…”

Handheld Near-Infrared Spectroscopy: State-of-the-Art Instrumentation and Applications in Material Identification, Food Authentication, and Environmental Investigations

“…Most of the above applications are primarily concerned with the rapid and secure on-site classification of unknown substances-sometimes hazardous or toxic-or authentication of the correct origin of goods by trained users-but not necessarily scientists [1][2][3][4]. Therefore, it seems that the realization has taken hold that vibrational spectroscopy-and as will be explained in more detail below, specifically handheld NIR instruments-can make a significant contribution to improving safety and general standards in everyday areas of life [5][6][7][8][9][10][11].…”