Building robust models for identification of adulteration in olive oil using FT-NIR, PLS-DA and variable selection

Fast quality assessment and origin identification of Gentianae Macrophyllae Radix using fourier transform infrared photoacoustic spectroscopy coupled with chemometrics

This study explores the application of Fourier Transform Infrared Photoacoustic Spectroscopy (FTIR-PAS) combined with chemometric analysis for rapid quality assessment and origin species verification of Gentianae Macrophyllae Radix (Qinjiao). Qinjiao, a traditional Chinese medicinal herb, faces significant challenges in quality control due to frequent adulteration and substitution. In this study, 195 Qinjiao samples were analyzed using FTIR-PAS, and chemometric techniques were employed for both classification and regression analysis. The results demonstrate that FTIR-PAS, integrated with chemometric methods, effectively differentiates Qinjiao samples by species. The combination of Detrending (DT) preprocessing and a K-Nearest Neighbors (KNN) classification model achieved an accuracy of 97.4 % in species identification. For quantitative analysis, Savitzky-Golay (SG) smoothing was the optimal preprocessing method for gentiopicroside, while DT was best for loganic acid, and no preprocessing was necessary for roburic acid. Additionally, the Competitive Adaptive Reweighted Sampling (CARS) algorithm, combined with models such as XGBoost and Random Forest (RF), significantly improved the prediction accuracy of key active components, with the highest RP2 reaching 0.84. This research underscores the potential of FTIR-PAS as a rapid, non-destructive approach for the quality assessment and authentication of traditional Chinese medicinal materials.

Rapid classification of Camellia seed varieties and non-destructive high-throughput quantitative analysis of fatty acids based on non-targeted fingerprint spectroscopy combined with chemometrics

Camellia oil is a high-quality vegetable oil rich in unsaturated fatty acids (FAs), with quality standardization challenged by the diversity of Camellia seed varieties. This study compared spectroscopy techniques (Near-Infrared [NIR] vs Mid-Infrared [MIR] spectroscopy) and analytical models (Discriminant Analysis [DA], Partial Least Squares [PLS], and Artificial Neural Networks [ANN]), seeking to classify Camellia seed varieties and estimate oil and principal FAs composition. The PCA analysis effectively discriminated among various Camellia seed varieties, likely due to variations in their oil and principal FAs compositions. Significantly, the NIR-based DA model significantly outperformed MIR, achieving 100 % accuracy in distinguishing Camellia seed varieties. In terms of predicting the oil and principal FAs compositions in Camellia seeds, NIR-based predictions models outperformed those derived from MIR, with PLS models surpassing ANN models. This study validated the potential of NIR technology combined with chemometrics for rapid, high-throughput, non-destructive identification of Camellia seeds.

Rapid metabolic fingerprinting meets machine learning models to identify authenticity and detect adulteration of essential oils with vegetable oils: Mentha and Ocimum study

Essential oils (EOs) are gaining popularity due to their potent antibacterial properties, as well as their applications in food preservation and flavor enhancement, offering growth opportunities for the food industry. However, their widespread use as food preservatives is limited by authenticity challenges, primarily stemming from adulteration with cheaper oils. This study investigated a rapid, cost-effective, and non-destructive method for assessing the authenticity of widely used Mentha and Ocimum EOs. The proposed approach integrates Fourier transform near-infrared (FT-NIR) spectroscopy with machine learning to enable rapid metabolic fingerprinting of EOs. Four Mentha species and three Ocimum species were analysed, and the system was tested on market samples adulterated with vegetable oils. The approach achieved exceptional performance, with Q2, R2, and accuracy exceeding 0.98, alongside specificity and sensitivity greater than 98 %. These findings demonstrated that FT-NIR, combined with machine learning, offers a highly efficient solution for addressing authenticity and adulteration issues in EOs.

Laser-Induced Breakdown Spectroscopy vs. Fluorescence Spectroscopy for Olive Oil Authentication

In the present work, laser-induced breakdown spectroscopy (LIBS) and fluorescence spectroscopy are used and assessed for the detection of EVOOs' adulteration with some non-EVOO edible oils (i.e., pomace, corn, sunflower, and soybean) and the discrimination of EVOOs based on geographical origin. For the direct comparison of the performance of the two techniques, the same set of EVOO samples was studied. The acquired spectroscopic data were analyzed by several machine learning algorithms, and the constructed predictive models are evaluated thoroughly for their reliability and robustness. In all cases, the high classification accuracies obtained support the potential and efficiency of both LIBS and fluorescence spectroscopy for the rapid, online, and in situ study of EVOOs' authentication issues, with LIBS being more advantageous as it operates much faster.

Discriminating the adulteration of varieties and misrepresentation of vintages of Pu'er tea based on Fourier transform near infrared diffuse reflectance spectroscopy

In the Pu'er tea market, the ubiquity of blending different varieties and the fraudulent representation of vintage years present a persistent challenge. Traditional sensory evaluation and experience are often inadequate for discerning the true variety and vintage of tea, highlighting the need for more sophisticated analytical methods to ensure authenticity and quality. Fourier transform near infrared diffuse reflectance spectroscopy combined with radial basis function neural network (RBFNN) was applied for determination of the varieties and vintages of Pu'er tea. For vintage identification, the accuracy, precision, recall, and F1-score of the RBFNN model for the prediction set were 99.2%, 98.2%, 98.0%, and 98.0%, respectively. For identification of varieties adulteration, the corresponding parameters were 98.9%, 97.2%, 96.7%, and 96.6%, respectively. These results illustrated the feasibility to identify the adulteration of varieties and misrepresentation of vintages of Pu'er tea with near infrared spectra and RBFNN model, proving an efficient alternative for Pu'er tea quality inspection, and offering a robust method for combating the pervasive issues within the market.

Adulteration detection of multi-species vegetable oils in camellia oil using Raman spectroscopy: Comparison of chemometrics and deep learning methods

Oil adulteration is a global challenge in the production of high value-added natural oils. Raman spectroscopy combined with mathematical modeling can be used for adulteration detection of camellia oil (CAO). In this study, the advantages of traditional chemometrics and deep learning methods in identifying and quantifying adulterated CAO were compared from a statistical perspective, and no significant difference were founded in the identification of CAO at different levels of adulteration. The recognition rate of pure and adulterated CAO was 100 %, but there were misclassifications among different adulterated CAOs. The deep learning models outperformed chemometrics methods in quantitative prediction of adulteration level, with RP2, RMSEP, and RPD of the optimal ConvLSTM model achieved 0.999, 0.9 % and 31.5, respectively. The classifiers and models developed in this study based on deep learning have wide applicability and reliability, and provide a fast and accurate method for adulteration detection in CAO.

Rapid and accurate detection of cinnamon oil adulteration in perilla leaf oil using atmospheric solids analysis probe-mass spectrometry

Perilla leaf oil (PLO) is a global premium vegetable oil with abundant nutrients and substantial economic value, rendering it susceptible to potential adulteration by unscrupulous entrepreneurs. The addition of cinnamon oil (CO) is one of the main adulteration avenues for illegal PLOs. In this study, new and real-time ambient mass spectrometric methods were developed to detect CO adulteration in PLO. First, atmospheric solids analysis probe tandem mass spectrometry combined with principal component analysis and principal component analysis-linear discriminant analysis was employed to differentiate between authentic and adulterated PLO. Then, a spectral library was established for the instantaneous matching of cinnamaldehyde in the samples. Finally, the results were verified using the SRM mode of ASAP-MS/MS. Within 3 min, the three methods successfully identified CO adulteration in PLO at concentrations as low as 5% v/v with 100% accuracy. The proposed strategy was successfully applied to the fraud detection of CO in PLO.

FT-NIR combined with machine learning was used to rapidly detect the adulteration of pericarpium citri reticulatae (chenpi) and predict the adulteration concentration

Pericarpium citri reticulatae (PCR) has been used as a food and spice for many years and is known for its rich nutritional content and unique aroma. However, price increases are often accompanied by adulteration. In this study, two kinds of adulterants (Orange peel-OP and Mandarin Rind-MR) were identified by chromaticity analysis, FT-NIR and machine learning algorithm, and the doping concentration was predicted quantitatively. The results show that colorimetric analysis cannot completely differentiate between PCR and adulterants. Using spectral preprocessing combined with machine learning algorithms, PCR and two adulterants were successfully distinguished, with classification accuracy reaching 99.30 % and 98.64 % respectively. After selecting characteristic wavelengths, the R2 P of the adulterated quantitative model is greater than 0.99. Generally, this study proposes to use FT-NIR to study the adulteration of PCR for the first time, which fills the technical gap in the adulteration research of PCR, and provides an important method to solve the increasingly serious adulteration problem of PCR.

Advances in Vibrational Spectroscopic Techniques for the Detection of Bio-Active Compounds in Virgin Olive Oils: A Comprehensive Review

Vibrational spectroscopic techniques have gained significant attention in recent years for their potential in the rapid and efficient analysis of virgin olive oils, offering a distinct advantage over traditional methods. These techniques are particularly valuable for detecting and quantifying bio-active compounds that contribute to the nutritional and health benefits of virgin olive oils. This comprehensive review explores the latest advancements in vibrational spectroscopic techniques applied to virgin olive oils, focusing on the detection and measurement of key bio-active compounds such as unsaturated fatty acids, phenolic compounds, and other antioxidant compounds. The review highlights the improvements in vibrational spectroscopy, data processing, and chemometric techniques that have significantly enhanced the ability to accurately identify these compounds compared to conventional analytical methods. Additionally, it addresses current challenges, including the need for standardized methodologies and the potential for integrating vibrational spectroscopy with other analytical techniques to improve accuracy and reliability. Finally, findings over the last two decades, in which vibrational spectroscopy techniques were effectively used for the detailed characterization of bio-active compounds in virgin olive oils, are discussed.

Integrating near-infrared hyperspectral imaging with machine learning and feature selection: Detecting adulteration of extra-virgin olive oil with lower-grade olive oils and hazelnut oil

Detecting adulteration in extra virgin olive oil (EVOO) is particularly challenging with oils of similar chemical composition. This study applies near-infrared hyperspectral imaging (NIR-HSI) and machine learning (ML) to detect EVOO adulteration with hazelnut, refined olive, and olive pomace oils at various concentrations (1%, 5%, 10%, 20%, 40%, and 100% m/m). Savitzky-Golay filtering, first and second derivatives, multiplicative scatter correction (MSC), standard normal variate (SNV), and their combinations were used to preprocess the spectral data, with Principal Component Analysis (PCA) reducing dimensionality. Classification was performed using Partial Least Squares-Discriminant Analysis (PLS-DA) and ML algorithms, including k-Nearest Neighbors (k-NN), Naïve Bayes, Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN). PLS-DA, k-NN, RF, SVM, NB, and ANN models achieved accuracy rates of 97.0-99.0%, 96.2-100%, 96.5-100%, 98.6-99.5%, 93.9-99.7%, and 99.2-100%, respectively, in discriminating between pure EVOO, adulterants, and adulterated oils. PLS-DA, RF, SVM, and ANN significantly outperformed Naïve Bayes (p < 0.05) in binary classification, with Matthews correlation coefficient (MCC) values exceeding 0.90. All the binary classifiers except Naïve Bayes, when coupled with SNV/MSC, Savitzky-Golay smoothing and derivatives, consistently achieved perfect scores (1.0) for accuracy, sensitivity, specificity, F1 score, precision, and MCC in distinguishing pure EVOO from adulterated oils. No significant differences (p > 0.05) in model performance were found between those using full spectra and those based on key variable selection. However, PLS-DA and ANN significantly outperformed k-NN, RF, and SVM (p < 0.05), with MCC values ranging from 0.95 to 1.00, indicating superior classification performance. These findings demonstrate that combining NIR-HSI with machine learning, along with key variable selection, potentially offers an effective, non-destructive solution for detecting adulteration in EVOO and combating fraud in the olive oil industry.

Exploring near-infrared spectroscopy and hyperspectral imaging as novel characterization methods for anaerobic gut fungi

Neocallimastigomycota are a phylum of anaerobic gut fungi (AGF) that inhabit the gastrointestinal tract of herbivores and play a pivotal role in plant matter degradation. Their identification and characterization with marker gene regions has long been hampered due to the high inter- and intraspecies length variability in the commonly used fungal marker gene region internal transcribed spacer (ITS). While recent research has improved methodology (i.e. switch to LSU D2 as marker region), molecular methods will always introduce bias through nucleic acid extraction or PCR amplification. Here, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) are introduced as two nucleic acid sequence-independent tools for the characterization and identification of AGF strains. We present a proof-of-concept for both, achieving an independent prediction accuracy of above 95% for models based on discriminant analysis trained with samples of three different genera. We further demonstrated the robustness of the NIRS model by testing it on cultures of different growth times. Overall, NIRS provides a simple, reliable, and nondestructive approach for AGF classification, independent of molecular approaches. The HSI method provides further advantages by requiring less biomass and adding spatial information, a valuable feature if this method is extended to mixed cultures or environmental samples in the future.

Trends in authentication of edible oils using vibrational spectroscopic techniques

The authentication of edible oils has become increasingly important for ensuring product quality, safety, and compliance with regulatory standards. Some prevalent authenticity issues found in edible oils include blending expensive oils with cheaper substitutes or lower-grade oils, incorrect labeling regarding the oil's source or type, and falsely stating the oil's origin. Vibrational spectroscopy techniques, such as infrared (IR) and Raman spectroscopy, have emerged as effective tools for rapidly and non-destructively analyzing edible oils. This review paper offers a comprehensive overview of recent advancements in using vibrational spectroscopy for authenticating edible oils. The fundamental principles underlying vibrational spectroscopy are introduced and chemometric approaches that enhance the accuracy and reliability of edible oil authentication are summarized. Recent research trends highlighted in the review include authenticating newly introduced oils, identifying oils based on their specific origins, adopting handheld/portable spectrometers and hyperspectral imaging, and integrating modern data handling techniques into the use of vibrational spectroscopic techniques for edible oil authentication. Overall, this review provides insights into the current state-of-the-art techniques and prospects for utilizing vibrational spectroscopy in the authentication of edible oils, thereby facilitating quality control and consumer protection in the food industry.

Hyperspectral identification of oil adulteration using machine learning techniques

Food adulteration is a global concern, drawing attention from safety authorities due to its potential health risks. Detecting and categorizing oil adulteration is crucial for consumer safety and food industry integrity. This research explores hyperspectral imaging (HSI) analysis to identify substandard oil adulteration at different stages. Using the non-destructive HSI Specim Fx 10 system, a method for precise and easy imaging-based fraud detection and classification was proposed. The 670 oil samples, including pure (Almond, Mustard, Coconut, Olive) and adulterated (Sunflower, Castor, Liquid Paraffin), were analyzed. The Savitzky-Golay filter preprocessed the images to remove noise and smooth spectral signatures. The oils were identified using various machine learning approaches, including Support Vector Machines, Logistic Regression, Linear Discriminant Analysis, Random Forests, Decision Trees, K-Nearest Neighbors, and Naïve Bayes with Linear Discriminant Analysis excelling in identification. Performance parameters, including precision, recall, F1-score, and overall accuracy, were calculated. The proposed method achieved a validation accuracy of 100%, outperforming numerous state-of-the-art approaches. This study introduces a robust pipeline for effective oil adulteration detection, offering a significant advancement in food safety and quality control.

Application of the FTIR technique as a non-invasive tool to discriminate Portuguese olive oils with Protected Designation of Origin

Three Portuguese olive oils with PDO ('Azeite do Alentejo Interior', 'Azeites da Beira Interior' and 'Azeite de Trás-os-Montes') were studied considering their physicochemical quality, antioxidant capacity, oxidative stability, total phenols content, gustatory sensory sensations and Fourier transform infrared (FTIR) spectra. All oils fulfilled the legal thresholds of EVOOs and the PDO's specifications. Olive oils from 'Azeite da Beira Interior' and 'Azeite de Trás-os-Montes' showed greater total phenols contents and antioxidant capacities, while 'Azeites da Beira Interior' presented higher oxidative stabilities. Linear discriminant models were developed using FTIR spectra (transmittance and the 1st and 2nd derivatives), allowing the correct identification of the oils' PDO (100 % sensitivity and specificity, repeated K-fold-CV). This study also revealed that multiple linear regression models, based on FTIR transmittance data, could predict the sweet, bitter, and pungent intensities of the PDO oils (R2 ≥0.979±0.016; RMSE≤0.26±0.05, repeated K-fold-CV). This demonstrates the potential of using FTIR as a non-destructive technique for authenticating oils with PDO.

Evaluation of different regression models for detection of adulteration of mustard and canola oil with argemone oil using fluorescence spectroscopy coupled with chemometrics

Mustard and canola oils are commonly used cooking oils in Asian countries such as India, Nepal, and Bangladesh, making them prone to adulteration. Argemone is a well-known adulterant of mustard oil, and its alkaloid sanguinarine has been linked with health conditions such as glaucoma and dropsy. Utilising a non-destructive spectroscopic method coupled with a chemometric approach can serve better for the detection of adulterants. This work aimed to evaluate the performance of various regression algorithms for the detection of argemone in mustard and canola oils. The spectral dataset was acquired from fluorescence spectrometer analysis of pure as well as adulterated mustard and canola oils with some local and commercial samples also. The prediction performance of the eight regression algorithms for the detection of adulterants was evaluated. Extreme gradient boosting regressor (XGBR), Category gradient boosting regressor (CBR), and Random Forest (RF) demonstrate potential for predicting adulteration levels in both oils with high R2 values.

Application for Identifying the Origin and Predicting the Physiologically Active Ingredient Contents of Gastrodia elata Blume Using Visible-Near-Infrared Spectroscopy Combined with Machine Learning

Gastrodia elata (G. elata) Blume is widely used as a health product with significant economic, medicinal, and ecological values. Due to variations in the geographical origin, soil pH, and content of organic matter, the levels of physiologically active ingredient contents in G. elata from different origins may vary. Therefore, rapid methods for predicting the geographical origin and the contents of these ingredients are important for the market. This paper proposes a visible-near-infrared (Vis-NIR) spectroscopy technology combined with machine learning. A variety of machine learning models were benchmarked against a one-dimensional convolutional neural network (1D-CNN) in terms of accuracy. In the origin identification models, the 1D-CNN demonstrated excellent performance, with the F1 score being 1.0000, correctly identifying the 11 origins. In the quantitative models, the 1D-CNN outperformed the other three algorithms. For the prediction set of eight physiologically active ingredients, namely, GA, HA, PE, PB, PC, PA, GA + HA, and total, the RMSEP values were 0.2881, 0.0871, 0.3387, 0.2485, 0.0761, 0.7027, 0.3664, and 1.2965, respectively. The Rp2 values were 0.9278, 0.9321, 0.9433, 0.9094, 0.9454, 0.9282, 0.9173, and 0.9323, respectively. This study demonstrated that the 1D-CNN showed highly accurate non-linear descriptive capability. The proposed combinations of Vis-NIR spectroscopy with 1D-CNN models have significant potential in the quality evaluation of G. elata.

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

The quality and safety of edible crops are key links inseparable from human health and nutrition. In the era of rapid development of artificial intelligence, using it to mine multi-source information on edible crops provides new opportunities for industrial development and market supervision of edible crops. This review comprehensively summarized the applications of multi-source data combined with machine learning in the quality evaluation of edible crops. Multi-source data can provide more comprehensive and rich information from a single data source, as it can integrate different data information. Supervised and unsupervised machine learning is applied to data analysis to achieve different requirements for the quality evaluation of edible crops. Emphasized the advantages and disadvantages of techniques and analysis methods, the problems that need to be overcome, and promising development directions were proposed. To monitor the market in real-time, the quality evaluation methods of edible crops must be innovated.

Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping

Currently, the presence of genetically modified (GM) organisms in agro-food markets is strictly regulated by enacted legislation worldwide. It is essential to ensure the traceability of these transgenic products for food safety, consumer choice, environmental monitoring, market integrity, and scientific research. However, detecting the existence of GM organisms involves a combination of complex, time-consuming, and labor-intensive techniques requiring high-level professional skills. In this paper, a concise and rapid pipeline method to identify transgenic rice seeds was proposed on the basis of spectral imaging technologies and the deep learning approach. The composition of metabolome across 3 rice seed lines containing the cry1Ab/cry1Ac gene was compared and studied, substantiating the intrinsic variability induced by these GM traits. Results showed that near-infrared and terahertz spectra from different genotypes could reveal the regularity of GM metabolic variation. The established cascade deep learning model divided GM discrimination into 2 phases including variety classification and GM status identification. It could be found that terahertz absorption spectra contained more valuable features and achieved the highest accuracy of 97.04% for variety classification and 99.71% for GM status identification. Moreover, a modified guided backpropagation algorithm was proposed to select the task-specific characteristic wavelengths for further reducing the redundancy of the original spectra. The experimental validation of the cascade discriminant method in conjunction with spectroscopy confirmed its viability, simplicity, and effectiveness as a valuable tool for the detection of GM rice seeds. This approach also demonstrated its great potential in distilling crucial features for expedited transgenic risk assessment.

Development of PCA-MLP Model Based on Visible and Shortwave Near Infrared Spectroscopy for Authenticating Arabica Coffee Origins

Arabica coffee, one of Indonesia's economically important coffee commodities, is commonly subject to fraud due to mislabeling and adulteration. In many studies, spectroscopic techniques combined with chemometric methods have been massively employed in classification issues, such as principal component analysis (PCA) and discriminant analyses, compared to machine learning models. In this study, spectroscopy combined with PCA and a machine learning algorithm (artificial neural network, ANN) were developed to verify the authenticity of Arabica coffee collected from four geographical origins in Indonesia, including Temanggung, Toraja, Gayo, and Kintamani. Spectra from pure green coffee were collected from Vis-NIR and SWNIR spectrometers. Several preprocessing techniques were also applied to attain precise information from spectroscopic data. First, PCA compressed spectroscopic information and generated new variables called PCs scores, which would become inputs for the ANN model. The discrimination of Arabica coffee from different origins was conducted with a multilayer perceptron (MLP)-based ANN model. The accuracy attained ranged from 90% to 100% in the internal cross-validation, training, and testing sets. The error in the classification process did not exceed 10%. The generalization ability of the MLP combined with PCA was superior, suitable, and successful for verifying the origin of Arabica coffee.

Visible-Near-Infrared Spectroscopy and Chemometrics for Authentication Detection of Organic Soybean Flour

Organic and non-organic soybean flours, although visually indifferent, have a significant difference in price and nutrition content. Therefore, the accurate authentication detection of organic soybean flour is necessary. Visible-near-infrared (Vis-NIR) spectroscopy coupled with chemometric methods is a non-destructive technique applied to detect authentic or adulterated organic soybean flour. The spectra of organic, adulterated organic, and non-organic soybean flours were captured using a Vis-NIR spectrometer at 350–1000 nm. The spectra were analyzed using partial least squares (PLS), principal component analysis (PCA), and the combination of these two with discriminant analysis (DA). The results showed that PCA using PC1 and PC2 could differentiate organic and non-organic soybean flours, whereas PC1 and PC4 can detect pure and adulterated organic soybean flours. The PCA–linear DA models showed 98.5% accuracy (Acc) for predicting pure organic and adulterated soybean flours and 100% Acc for predicting organic and non-organic flours. Moreover, PLS regression models resulted in a high R² of >95% for predicting organic and non-organic flours and pure and adulterated soybean flours. In addition, the PLS-DA models can differentiate organic from non-organic soybean flour and distinguish pure and adulterated soybean flours with 100% Acc and reliability.

A comparative study on classification of edible vegetable oils by infrared, near infrared and fluorescence spectroscopy combined with chemometrics

Driven by economic benefits like any other foods, vegetable oil has long been plagued by mislabeling and adulteration. Many studies have addressed the field of classification and identification of vegetable oils by various analysis techniques, especially spectral analysis. A comparative study was performed using Fourier transform infrared spectroscopy (FTIR), visible near-infrared spectroscopy (Vis-NIR) and excitation-emission matrix fluorescence spectroscopy (EEMs) combined with chemometrics to distinguish different types of edible vegetable oils. FTIR, Vis-NIR and EEMs datasets of 147 samples of five vegetable oils from different brands were analyzed. Two types of pattern recognition methods, principal component analysis (PCA)/multi-way principal component analysis (M-PCA) and partial least squares discriminant analysis (PLS-DA)/multilinear partial least squares discriminant analysis (N-PLS-DA), were used to resolve these data and distinguish vegetable oil types, respectively. PCA/M-PCA analysis exhibited that three spectral data of five vegetable oils showed a clustering trend. The total correct recognition rate of the training set and prediction set of FTIR spectra of vegetable oil based on PLS-DA method are 100%. The total recognition rate of Vis-NIR based on PLS-DA are 100% and 97.96%. However, the total correct recognition rate of training set and prediction set of EEMs data based on N-PLS-DA method is 69.39% and 75.51%, respectively. The comparative study showed that FTIR and Vis-NIR combined with chemometrics were more suitable for vegetable oil species identification than EEMs technique. The reason may be concluded that almost all chemical components in vegetable oil can produce FTIR and NIR absorption, while only a small amount of fluorophores can produce fluorescence. That is, FTIR and NIR can provide more spectral information than EEMs. Analysis of EEMs data using self-weighted alternating trilinear decomposition (SWATLD) also showed that fluorophores were a few and irregularly distributed in vegetable oils.

Rapid Identification of Fupenzi (Rubus chingii Hu) and Its Adulteration by AuNP Visualization

Fupenzi (Rubus chingii Hu) is a dried and immature fruit in East China, which has effects of nourishing kidneys, solidifying essence, and otherwise. Because Fupenzi was often adulterated and replaced with inferior things, this paper had researched Fupenzi and its adulterant raspberry. A new type of visible sensor was constructed by using Au nanoparticles (AuNPs), which was modified by the surface-active agent and combined with the ultraviolet-visible (UV-vis) spectrum technology. It was found that the change in particle size after the interaction of AuNPs and adulterants will lead to color change. In this paper, the RGB (red, green, and blue) values of the solution were extracted to correlate the color with the concentration of adulterants, and the relationship between the absorption peak intensity and the concentration of adulterants was established. The results showed that the intensity of an absorption peak is related to adulteration concentration, and the color of the solution changed from red to gray as the particle size changed. The visual sensor constructed based on the above principle is a fast and precise method to detect adulteration with different concentrations, which has a potential application value in real-time and rapid detection of Fupenzi’s quality.

Variable selection in the chemometric treatment of food data: A tutorial review

Food analysis covers aspects of quality and detection of possible frauds to ensure the integrity of the food. The arsenal of analytical instruments available for food analysis is broad and allows the generation of a large volume of information per sample. But this instrumental information may not yet give the desired answer; it must be processed to provide a final answer for decision making. The possibility of discarding non-informative and/or redundant signals can lead to models of better accuracy, robustness, and chemical interpretability, in line with the principle of parsimony. Thus, in this tutorial review, we cover aspects of variable selection in food analysis, including definitions, theoretical aspects of variable selection, and case studies showing the advantages of variable selection-based models concerning the use of a wide range of non-informative and redundant instrumental information in the analysis of food matrices.

Advanced process analytical tools for identification of adulterants in edible oils - A review

This review summarizes the use of spectroscopic processes-based analytical tools coupled with chemometric techniques for the identification of adulterants in edible oil. Investigational approaches of process analytical tools such asspectroscopy techniques, nuclear magnetic resonance (NMR), hyperspectral imaging (HSI), e-tongue and e-nose combined with chemometrics were used to monitor quality of edible oils. Owing to the variety and intricacy of edible oil properties along with the alterations in attributes of the PAT tools, the reliability of the tool used and the operating factors are the crucial components which require attention to enhance the efficiency in identification of adulterants. The combination of process analytical tools with chemometrics offers a robust technique with immense chemotaxonomic potential. These involves identification of adulterants, quality control, geographical origin evaluation, process evaluation, and product categorization.

Real-time monitoring of the column chromatographic process of Phellodendri Chinensis Cortex part II: multivariate statistical process control based on near-infrared spectroscopy

Multivariate statistical process control has been successfully used for the real-time monitoring of the column chromatographic process of Phellodendri Chinensis Cortex.

Physicochemical parameters combined flash GC e-nose and artificial neural network for quality and volatile characterization of vinegar with different brewing techniques

Vinegar is a kind of traditional fermented food, there are significant variances in quality and flavor due to differences in raw ingredients and processes. The quality assessment and flavor characteristics of 69 vinegar samples with 5 brewing processes were analyzed by physicochemical parameters combined with flash gas chromatography (GC) e-nose. The evaluation system of quality and the detection method of flavor profile were established. 17 volatile flavor compounds and potential flavor differential compounds of each brewing process were identified. The artificial neural network (ANN) analysis model was established based on the physicochemical parameters and the analysis of flash GC e-nose. Although the physicochemical parameters were more intuitive in quality evaluating, the flash GC e-nose could better reflect the flavor characteristics of vinegar samples and had better fitting, prediction and discrimination ability, the correct rates of training and prediction of flash GC e-nose trained ANN model were 98.6% and 96.7%, respectively.

Measurement Strategies for the Classification of Edible Oils Using Low-Cost Miniaturised Portable NIR Instruments

Miniaturised near-infrared (NIR) instruments have been increasingly used in the last few years, and they have become useful tools for many applications on different types of samples. The market already offers a wide variety of these instruments, each one having specific requirements for the correct acquisition of the instrumental signal. This paper presents the development and optimisation of different measuring strategies for two miniaturised NIR instruments in order to find the best measuring conditions for the rapid and low-cost analysis of olive oils. The developed strategies have been applied to the classification of different samples of olive oils, obtaining good results in all cases.