Use of near-infrared spectroscopy combined with chemometrics for authentication and traceability of intact lemon fruits

Geographic traceability of Gastrodia elata Blum based on combination of NIRS and Chemometrics

The content of the active ingredient in G. elata Bl. is affected by the soil and climate of different regions, so geographical traceability is essential to ensure its quality, commercial value. This study used a combination of NIRS and various chemometric methods to establish an effective geotraceability method for G. elata Bl.. Firstly, a traditional machine learning model was built based on the SF dataset NIRS, and a ResNet model was built based on NIRS generated 2DCOS images and 3DCOS images. Secondly, the model performance was validated using the ZT dataset. The results show that the 3DCOS-ResNet model performs the best with 100.00 % and 95.45 % test set and EV accuracy, respectively. This study provides a theoretical basis for regulators to quickly ensure the authenticity of G. elata Bl. sources. However, more data and in-depth studies are needed in the future to validate and improve the applicability of the model.

Advances in Computer Vision and Spectroscopy Techniques for Non-Destructive Quality Assessment of Citrus Fruits: A Comprehensive Review

Citrus fruits, classified under the Rutaceae family and Citrus genus, are valued for their high nutritional content, attributed to their rich array of natural bioactive compounds. To ensure both quality and nutritional value, precise non-destructive testing methods are crucial. Among these, computer vision and spectroscopy technologies have emerged as key tools. This review examines the principles and applications of computer vision technologies-including traditional computer vision, hyperspectral, and multispectral imaging-as well as various spectroscopy techniques, such as infrared, Raman, fluorescence, terahertz, and nuclear magnetic resonance spectroscopy. Additionally, data fusion methods that integrate these technologies are discussed. The review explores innovative uses of these approaches in Citrus quality inspection and grading, damage detection, adulteration identification, and traceability assessment. Each technology offers distinct characteristics and advantages tailored to the specific testing requirements in Citrus production. Through data fusion, these technologies can be synergistically combined, enhancing the accuracy and depth of Citrus quality assessments. Future advancements in this field will likely focus on optimizing data fusion algorithms, selecting effective preprocessing and feature extraction techniques, and developing portable, on-site detection devices. These innovations will drive the Citrus industry toward increased intelligence and precision in quality control.

Strontium isotope signature of the PGI lemons Limone Costa d'Amalfi and Limone di Sorrento, and of the orchard soils from Sorrento peninsula

The Limone Costa d'Amalfi and Limone di Sorrento lemons from the Sorrento Peninsula have Protected Geographical Indication (PGI) and are subject to origin fraud. The 87Sr/86Sr ratio (SrIR) signature of lemons and soils was investigated to verify its reliability to trace the PGI lemons and to highlight environmental factors responsible of the lemon SrIRs. The SrIR ranges of each PGI lemon were not discriminating as they overlapped with each other and some non-PGI lemon SrIRs fell within these ranges. The lemon SrIRs were generally not correlated with bulk and bioavailable soil SrIRs, rather, they were the result of plant Sr uptake with different SrIRs depending on interaction between water supplied to soil and soil with different chemical and physical characteristics. The study of lemon SrIRs and the causes of their origin and variability provides a reliable forecast reference for the other PGI agri-food products in the study area.

Differentiation of Polygonatum Cyrtonema Hua from Different Geographical Origins by Near-Infrared Spectroscopy with Chemometrics

BACKGROUND

The identification of the geographical origin of Polygonatum cyrtonema Hua is of particular importance because the quality and market value of Polygonatum cyrtonema Hua from different production areas are highly variable due to differences in the growing environment and climatic conditions.

OBJECTIVE

This study utilized near-infrared spectra (NIR) of Polygonatum cyrtonema Hua (n = 400) to develop qualitative models for effective differentiation of Polygonatum cyrtonema Hua from various regions.

METHODS

The models were produced under different conditions to distinguish the origins distinctly. Ten preprocessing methods have been used to preprocess the original spectra (OS) and to select the most optimal spectral preprocessing method. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to determine appropriate models. For simplicity, the pretreated full spectrum was calculated by different wavelength selection methods, and the four most significant variables were selected as discriminant indicator variables.

RESULTS

The results show that Polygonatum cyrtonema Hua from different regions can be effectively distinguished using spectra from a series of samples analyzed by OPLS-DA. The accuracy of the OPLS-DA model is also satisfactory, with a good differentiation rate.

CONCLUSION

The study findings indicate the feasibility of using spectroscopy in combination with multivariate analysis to identify the geographical origins of Polygonatum cyrtonema Hua.

HIGHLIGHTS

The utilization of NIR spectroscopy combined with chemometrics exhibits high efficacy in discerning the provenance of herbal medicines and foods, thereby facilitating QA measures.

Insights on the Extraction and Analysis of Phenolic Compounds from Citrus Fruits: Green Perspectives and Current Status

Citrus fruits (CF) are highly consumed worldwide, fresh, processed, or prepared as juices and pies. To illustrate the high economic importance of CF, the global production of these commodities in 2021 was around 98 million tons. CF's composition is considered an excellent source of phenolic compounds (PC) as they have a large amount and variety. Since ancient times, PC has been highlighted to promote several benefits related to oxidative stress disorders, such as chronic diseases and cancer. Recent studies suggest that consuming citrus fruits can prevent some of these diseases. However, due to the complexity of citrus matrices, extracting compounds of interest from these types of samples, and identifying and quantifying them effectively, is not a simple task. In this context, several extractive and analytical proposals have been used. This review discusses current research involving CF, focusing mainly on PC extraction and analysis methods, regarding advantages and disadvantages from the perspective of Green Chemistry.

A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition

Lanxangia tsaoko's accurate classifications of different origins and fruit shapes are significant for research in L. tsaoko difference between origin and species as well as for variety breeding, cultivation, and market management. In this work, Fourier transform-near infrared (FT-NIR) spectroscopy was transformed into two-dimensional and three-dimensional correlation spectroscopies to further investigate the spectral characteristics of L. tsaoko. Before building the classification model, the raw FT-NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS-DA and SVM models can achieve 100% classification in origin classification, but what is difficult to avoid is the complex process of model optimization. The ResNet image recognition model classifies the origin and shape of L. tsaoko with 100% accuracy, and without the need for complex preprocessing and feature extraction, the model facilitates the realization of fast, accurate, and efficient identification.

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Quality analysis and geographical origin identification of Rosa roxburghii Tratt from three regions based on Fourier transform infrared spectroscopy

The study aimed to provide new information of Rosa roxburghii Tratt (RRT) for the production of functional foods and distinguish the geographical origins of RRT. The nutritional components of RRT from three regions in China, such as vitamin C, polysaccharides, total flavonoids, and total phenolics, and their antioxidant activities were analyzed by one-way ANOVA. The results of Fourier transform infrared spectroscopy (FT-IR) combined with principal component analysis (PCA), stepwise linear discriminant analysis (SLDA), k-nearest neighbor (k-NN), and support vector machine (SVM) were used to establish discriminant models to identify the geographical origin of RRT. The results of one-way ANOVA showed that the contents of some nutrients and antioxidant activity were significantly different among RRT from different regions and their FT-IR spectra also showed significant differences. The characteristic fingerprint bands of FT-IR (1679-1618 cm^-1and 1520-900 cm^-1) closely related to the geographical origins of RRT were screened out. Based on SLDA, a discriminant model was established to realize the classification and identification of RRT from different regions and the correct discrimination rate of the testing sample set obtained with the established model reached 100 %. Geographical factors caused the obvious differences in nutritional components and antioxidant activity in RRT. The characteristic fingerprint bands of RRT obtained with FT-IR could be used to identify the geographical origins of RRT more quickly and accurately.

Implementation of relevant fourth industrial revolution innovations across the supply chain of fruits and vegetables: A short update on Traceability 4.0

Food Traceability 4.0 refers to the application of fourth industrial revolution (or Industry 4.0) technologies to ensure food authenticity, safety, and high food quality. Growing interest in food traceability has led to the development of a wide range of chemical, biomolecular, isotopic, chromatographic, and spectroscopic methods with varied performance and success rates. This review will give an update on the application of Traceability 4.0 in the fruits and vegetables sector, focusing on relevant Industry 4.0 enablers, especially Artificial Intelligence, the Internet of Things, blockchain, and Big Data. The results show that the Traceability 4.0 has significant potential to improve quality and safety of many fruits and vegetables, enhance transparency, reduce the costs of food recalls, and decrease waste and loss. However, due to their high implementation costs and lack of adaptability to industrial environments, most of these advanced technologies have not yet gone beyond the laboratory scale. Therefore, further research is anticipated to overcome current limitations for large-scale applications.

Vis-NIR Spectroscopy Combined with GAN Data Augmentation for Predicting Soil Nutrients in Degraded Alpine Meadows on the Qinghai-Tibet Plateau

Soil nutrients play vital roles in vegetation growth and are a key indicator of land degradation. Accurate, rapid, and non-destructive measurement of the soil nutrient content is important for ecological conservation, degradation monitoring, and precision farming. Currently, visible and near-infrared (Vis-NIR) spectroscopy allows for rapid and non-destructive monitoring of soil nutrients. However, the performance of Vis-NIR inversion models is extremely dependent on the number of samples. Limited samples may lead to low prediction accuracy of the models. Therefore, modeling and prediction based on a small sample size remain a challenge. This study proposes a method for the simultaneous augmentation of soil spectral and nutrient data (total nitrogen (TN), soil organic matter (SOM), total potassium oxide (TK₂O), and total phosphorus pentoxide (TP₂O₅)) using a generative adversarial network (GAN). The sample augmentation range and the level of accuracy improvement were also analyzed. First, 42 soil samples were collected from the pika disturbance area on the QTP. The collected soils were measured in the laboratory for Vis-NIR and TN, SOM, TK₂O, and TP₂O₅ data. A GAN was then used to augment the soil spectral and nutrient data simultaneously. Finally, the effect of adding different numbers of generative samples to the training set on the predictive performance of a convolutional neural network (CNN) was analyzed and compared with another data augmentation method (extended multiplicative signal augmentation, EMSA). The results showed that a GAN can generate data very similar to real data and with better diversity. A total of 15, 30, 60, 120, and 240 generative samples (GAN and EMSA) were randomly selected from 300 generative samples to be included in the real data to train the CNN model. The model performance first improved and then deteriorated, and the GAN was more effective than EMSA. Further shortening the interval for adding GAN data revealed that the optimal ranges were 30-40, 50-60, 30-35, and 25-35 for TK₂O, TN, TP₂O₅, and SOM, respectively, and the validation set accuracy was maximized in these ranges. Therefore, the above method can compensate to some extent for insufficient samples in the hyperspectral prediction of soil nutrients, and can quickly and accurately estimate the content of soil TK₂O, TN, TP₂O₅, and SOM.

Rapid determination of chemical components and antioxidant activity of the fruit of Crataegus pinnatifida Bunge by NIRS and chemometrics

OBJECTIVE

To establish a method for quality evaluation of the fruit of Crataegus pinnatifida Bunge, also known as Shanzha, by near-infrared spectroscopy combined with chemometrics.

METHOD

Seventy-two batches of Shanzha samples were collected, and the content of total components (flavonoids, phenols and organic acids), monomer components (chlorogenic acid, hyperoside and isoquercitrin), as well as the antioxidant activity of 60% ethanol extract were determined by usual methods. Then, all measured values were correlated with the near infrared spectra of Shanzha, and the partial least squares regression models were established. As to improve the model performance, various methods for spectra pretreatment and wavelength selection were investigated.

RESULTS

After optimization, the models obtained the coefficients of determination in both calibration and prediction >0.9, and the residual prediction deviations >3, indicating that the models had good prediction abilities.

CONCLUSION

The present method can serve as an alternative to the methods for comprehensive and rapid quality evaluation of Shanzha.

Near-Infrared data classification at phone terminal based on the combination of PCA and CS-RBFSVC algorithms

Near-infrared (NIR) spectroscopy is a non-destructive, efficient and convenient detection technology, with the emergence of portable NIR spectrometers, NIR mobile applications (APPs) come into being. The popularity of intelligent mobile phones provides an impetus to the research and development of NIR APPs, however, the primary functions such as operating the NIR spectrometers and collecting data cannot satisfy NIR users in the field of data processing. Herein, we propose an APP processing NIR data locally at the mobile terminal, by the comprehensive utilization of Principal Component Analysis (PCA) and Cuckoo Search algorithm optimized Support Vector Classifier with radial basis function (RBFSVC) kernel (CS-RBFSVC). 738 NIR samples of four drugs (Cydiodine Buccal Tablets, Sulfasalazine Enteric-coated Tablets, Dexamethasone Acetate Tablets, Vecuronium Bromide for Injection) were used as the validation objects to train and test the data classification model. Firstly, the original data were subjected to dimensional reduction through PCA for the purpose of compressing calculation amount. Secondly, the CS-RBFSVC model was utilized to classify the types of drugs and their manufacturers, moreover, the improved accuracy and efficiency by introducing Cuckoo Search (CS) algorithm into RBFSVC were proven in comparison with the conventional grid optimized RBFSVC (Grid-RBFSVC) and Linear Support Vector Classifier (Linear-SVC). Last but not least, an APP based on the proposed PCA and CS-RBFSVC model is developed and demonstrated to be able to classify the type of drugs with an accuracy of 100%, the accuracies of classifying the drugs' manufacturers were 100%, 100%, 98.3% and 90.7%, respectively. Conclusively, the proposed PCA and CS-RBFSVC based model can provide a low-consumption, high accuracy and quick strategy for NIR data classification and overcome the limitations of internal storage and operating speed at phone terminals, in conjunction with the portable NIR spectrometer, it is believed to push forward NIR technology into the instant detection and on-site inspection.

Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives

Herbal medicines (HMs) have been utilized to prevent and treat human ailments for thousands of years. Especially, HMs have recently played a crucial role in the treatment of COVID-19 in China. However, HMs are susceptible to various factors during harvesting, processing, and marketing, affecting their clinical efficacy. Therefore, it is necessary to conclude a rapid and effective method to study HMs so that they can be used in the clinical setting with maximum medicinal value. Non-targeted analytical technology is a reliable analytical method for studying HMs because of its unique advantages in analyzing unknown components. Based on the extensive literature, the paper summarizes the benefits, limitations, and applicability of non-targeted analytical technology. Moreover, the article describes the application of non-targeted analytical technology in HMs from four aspects: structure analysis, authentication, real-time monitoring, and quality assessment. Finally, the review has prospected the development trend and challenges of non-targeted analytical technology. It can assist HMs industry researchers and engineers select non-targeted analytical technology to analyze HMs' quality and authenticity.

Determination of Pork Meat Storage Time Using Near-Infrared Spectroscopy Combined with Fuzzy Clustering Algorithms

The identification of pork meat quality is a significant issue in food safety. In this paper, a novel strategy was proposed for identifying pork meat samples at different storage times via Fourier transform near-infrared (FT-NIR) spectroscopy and fuzzy clustering algorithms. Firstly, the FT-NIR spectra of pork meat samples were collected by an Antaris II spectrometer. Secondly, after spectra preprocessing with multiplicative scatter correction (MSC), the orthogonal linear discriminant analysis (OLDA) method was applied to reduce the dimensionality of the FT-NIR spectra to obtain the discriminant information. Finally, fuzzy C-means (FCM) clustering, K-harmonic means (KHM) clustering, and Gustafson–Kessel (GK) clustering were performed to establish the recognition model and classify the feature information. The highest clustering accuracies of FCM and KHM were both 93.18%, and GK achieved a clustering accuracy of 65.90%. KHM performed the best in the FT-NIR data of pork meat considering the clustering accuracy and computation. The overall experiment results demonstrated that the combination of FT-NIR spectroscopy and fuzzy clustering algorithms is an effective method for distinguishing pork meat storage times and has great application potential in quality evaluation of other kinds of meat.