Rapid detection of the authenticity and adulteration of sesame oil using excitation-emission matrix fluorescence and chemometric methods

Rapid identification of Radix Astragali origin by using fluorescence probe combined with chemometrics

Fluorescent probes for metal ion recognition can be divided into selective probes, weakly selective probes, and non-selective probes roughly. Weakly selective probes are not often used for quantitative analysis of metal ions due to their overlapping spectra resulting from simultaneous interactions with multiple metal ions. Conversely, the different metal ions contained in herbal medicine extracts from different geographical origins will produce corresponding fluorescence fingerprint profiles after interaction with weakly selective fluorescence probes. The performance can be used in the study of origin tracing of food or Chinese herbal medicine. Weakly selective fluorescent probes of benzimidazole derivatives have been synthesized and attempted to be used in the origin tracing of Radix Astragali in this work. Radix Astragali from different origins will produce different fluorescence fingerprint spectra due to the difference of metal ions and content in combination with the probe. Excitation-emission matrix (EEM) fluorescence spectroscopy in conjunction with N-way partial least squares discriminant analysis (N-PLS-DA), and unfolded partial least squares discriminant analysis (U-PLS-DA) were used to identify the origin of 150 Radix Astragali samples from five geographical origins. The prediction results showed that the correct recognition rates of the U-PLS-DA model and N-PLS-DA model are 95.92% and 93.88%, respectively. In comparison, the results of U-PLS-DA are slightly better than those of N-PLS-DA. These findings indicate that EEM fluorescence spectroscopy based on weakly selective fluorescent probes combined with multi-way chemometrics provides a good idea for the origin tracing of traditional Chinese medicine.

Application of flash GC e-nose and FT-NIR combined with deep learning algorithm in preventing age fraud and quality evaluation of pericarpium citri reticulatae

Pericarpium citri reticulatae (PCR) is the dried mature fruit peel of Citrus reticulata Blanco and its cultivated varieties in the Brassicaceae family. It can be used as both food and medicine, and has the effect of relieving cough and phlegm, and promoting digestion. The smell and medicinal properties of PCR are aged over the years; only varieties with aging value can be called "Chenpi". That is to say, the storage year of PCR has a great influence on its quality. As the color and smell of PCR of different storage years are similar, some unscrupulous merchants often use PCRs of low years to pretend to be PCRs of high years, and make huge profits. Therefore, we did this study with the aim of establishing a rapid and nondestructive method to identify the counterfeiting of PCR storage year, so as to protect the legitimate rights and interests of consumers. In this study, a classification model of PCR was established by e-eye, flash GC e-nose, and Fourier transform near-infrared (FT-NIR) combined with machine learning algorithms, which can quickly and accurately distinguish PCRs of different storage years. DFA and PLS-DA models were established by flash GC e-nose to distinguish PCRs of different ages, and 8 odor components were identified, among which (+)-limonene and γ-terpinene were the key components to distinguish PCRs of different ages. In addition, the classification and calibration model of PCRs were established by the combination of FT-NIR and machine learning algorithms. The classification models included SVM, KNN, LSTM, and CNN-LSTM, while the calibration models included PLSR, LSTM, and CNN-LSTM. Among them, the CNN-LSTM model built by internal capsule had significantly better classification and calibration performance than the other models. The accuracy of the classification model was 98.21 %. The R2P of age, (+)-limonene and γ-terpinene was 0.9912, 0.9875 and 0.9891, respectively. These results showed that the combination of flash GC e-nose and FT-NIR combined with deep learning algorithm could quickly and accurately distinguish PCRs of different ages. It also provided an effective and reliable method to monitor the quality of PCR in the market.

Aromatic Fingerprints: VOC Analysis with E-Nose and GC-MS for Rapid Detection of Adulteration in Sesame Oil

Food quality assurance is an important field that directly affects public health. The organoleptic aroma of food is of crucial significance to evaluate and confirm food quality and origin. The volatile organic compound (VOC) emissions (detectable aroma) from foods are unique and provide a basis to predict and evaluate food quality. Soybean and corn oils were added to sesame oil (to simulate adulteration) at four different mixture percentages (25-100%) and then chemically analyzed using an experimental 9-sensor metal oxide semiconducting (MOS) electronic nose (e-nose) and gas chromatography-mass spectroscopy (GC-MS) for comparisons in detecting unadulterated sesame oil controls. GC-MS analysis revealed eleven major VOC components identified within 82-91% of oil samples. Principle component analysis (PCA) and linear detection analysis (LDA) were employed to visualize different levels of adulteration detected by the e-nose. Artificial neural networks (ANNs) and support vector machines (SVMs) were also used for statistical modeling. The sensitivity and specificity obtained for SVM were 0.987 and 0.977, respectively, while these values for the ANN method were 0.949 and 0.953, respectively. E-nose-based technology is a quick and effective method for the detection of sesame oil adulteration due to its simplicity (ease of application), rapid analysis, and accuracy. GC-MS data provided corroborative chemical evidence to show differences in volatile emissions from virgin and adulterated sesame oil samples and the precise VOCs explaining differences in e-nose signature patterns derived from each sample type.

Classification of Chinese traditional cereal vinegars and antioxidant property predication by fluorescence spectroscopy

In this work, a rapid and accurate strategy for classification of Chinese traditional cereal vinegars (CTCV) and antioxidant property predication was proposed by using the combination fluorescence spectroscopy and machine learning. Three characteristic fluorescent components were extracted by parallel factor analysis (PARAFAC), which have correlations greater than 0.8 with antioxidant activity of CTCV obtained by Pearson correlation analysis. Machine learning methods, including linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA) and N-way partial least squares discriminant analysis (N-PLS-DA), were used for the classification of different types of CTCV, and the correct classification rates was higher than 97%. The antioxidant property of CTCV were further quantified by using optimized variable-weighted least-squares support vector machine based on particle swarm optimization (PSO-VWLS-SVM). The proposed strategy provides a basis for further research on antioxidant active ingredients and antioxidant mechanisms of CTCV, and enable the continued exploration and application of CTCV from different types.

Rapid qualitative detection of titanium dioxide adulteration in persimmon icing using portable Raman spectrometer and Machine learning

Persimmon icing is the white crystalline powder that adheres to the surface of persimmon cakes when the sugar in the persimmon spills over during processing, which is considered the essence of persimmon. Titanium dioxide is a food additive that is commonly added to the surface of persimmon cakes to impersonate high-quality persimmon cakes. However, excessive titanium dioxide can be harmful to humans, so a quick method is needed to identify persimmon cakes as adulterated. Raman spectroscopy with distinctive advantages of water-insensitivity, real-time, field-deployable, label-free, and fingerprinting-identification has been rapidly developed and used in food quality assurance and safety monitoring. In this study, we investigated Raman spectroscopy integrated with machine learning to assess titanium dioxide adulteration in dried persimmon icing. The adaptive iterative reweighting partial least squares (air-PLS) algorithm as an effective algorithm was used to remove fluorescent background signals in raw Raman spectroscopy. Principal components analysis (PCA) was employed to analyze the spectral data and determine the class memberships, and results showed that 99.9% of information could be explained by PC-1 and PC-2. Compared with extreme learning machine (ELM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), and random forest (RF) models, one-dimensional stack auto encoder convolutional neural network (1D-SAE-CNN) could provide the highest detection accuracy of 0.9825, precision of 0.9824, recall of 0.9825, and f1-score of 0.9824. This study shows that Raman spectroscopy coupled with 1D-SAE-CNN is a promising method to detect titanium dioxide adulteration in persimmon icing.

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.

Tracing sources of oilfield wastewater based on excitation-emission matrix fluorescence spectroscopy coupled with chemical pattern recognition techniques

In order to prevent the illegal discharge of oilfield wastewater, this work proposed excitation-emission matrix fluorescence (EEMF) spectroscopy coupled with two kinds of chemical pattern recognition methods for tracing the sources of oilfield wastewater. The first pattern recognition method was built from the relative concentrations extracted by alternating trilinear decomposition (ATLD) based on partial least squares-discriminant analysis (PLS-DA) algorithm, and the other one was modeled based on strictly multi-way partial least squares-discriminant analysis (N-PLS-DA). Both methods showed good discrimination abilities for oilfield wastewater samples from three different sources. The total recognition rates of the training and prediction sets are 100%, the values of sensitivity and selectivity are 1. This study showed that EEMF spectroscopy combined with chemical pattern recognition techniques could be used as a potential tool for tracing the sources of oilfield wastewater.

Rapid identification of the geographical origin of Eucommia ulmoides by using excitation-emission matrix fluorescence combined with chemometric methods

Eucommia ulmoides is an important and valuable traditional Chinese medicine with various medical functions, and has been widely used as health food in China, Japan, South Korea and other Asian countries for many years. The efficacy and quality of E. ulmoides are closely associated with the geographical origin. In this work, the potential of excitation-emission matrix (EEMs) fluorescence coupled with chemometric methods was investigated for simple, rapid and accurate for identification E. ulmoides from different geographical origins. Parallel factor analysis (PARAFAC) was applied for characterizing the fluorescence fingerprints of E. ulmoides samples. Moreover, k-nearest neighbor (kNN), principal component analysis-linear discriminant analysis (PCA-LDA) and partial least squares discriminant analysis (PLS-DA) models were used for the classification of E. ulmoides samples according to their geographical origins. The results showed that kNN model was more suitable for identification of E. ulmoides samples from different provinces. The kNN model could identify E. ulmoides samples from eight different geographical origins with 100% accuracy on the training and test sets. Therefore, the proposed method was available for conveniently and accurately determining the geographical origin of E. ulmoides, which can expect to be an attractive alternative method for identifying the geographic origin of other traditional Chinese medicines.

Non-destructive detection and classification of textile fibres based on hyperspectral imaging and 1D-CNN

Textile fibre is very common in daily life, and its classification and identification play an important role in textile recycling, archaeology, public security, and other industries. However, traditional identification methods are time-consuming, laborious, and often destructive to the samples. In order to quickly, accurately, and nondestructively classify and recognize textile fibres, this study established a textile fibre classification and recognition method based on hyperspectral imaging (HSI) and a one-dimensional convolutional neural network (1D-CNN) model. Hyperspectral images of 25 kinds of commercial textile fibres were collected and denoised by pixel fusion. Four traditional machine learning classification models, k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), and partial least squares-discriminant analysis (PLS-DA), were used to identify the data. The results show that RF has the highest classification accuracy, reaching 91.4%. Then a back propagation neural network (BPNN) model and a one-dimensional convolutional neural network (1D-CNN) model were constructed and compared with the traditional machine learning methods. The results show that the 1D-CNN models have 97.9% and 98.6% accuracy on the training and test sets, respectively. The precision (Pr), sensitivity (Se), specificity (Sp), and F1 score (F1 score) of the models reached 98.7%, 98.6%, 99.9%, and 98.6%, respectively, which were significantly better than the four traditional machine learning models. It seems that 1D-CNN combined with the HSI technique may be a potential method in the detection and classification of textile fibres.

Distinguishing Different Varieties of Oolong Tea by Fluorescence Hyperspectral Technology Combined with Chemometrics

Oolong tea is a semi-fermented tea that is popular among people. This study aims to establish a classification method for oolong tea based on fluorescence hyperspectral technology(FHSI) combined with chemometrics. First, the spectral data of Tieguanyin, Benshan, Maoxie and Huangjingui were obtained. Then, standard normal variation (SNV) and multiple scatter correction (MSC) were used for preprocessing. Principal component analysis (PCA) was used for data visualization, and with tolerance ellipses that were drawn according to Hotelling, outliers in the spectra were removed. Variable importance for the projection (VIP) > 1 in partial least squares discriminant analysis (PLS−DA) was used for feature selection. Finally, the processed spectral data was entered into the support vector machine (SVM) and PLS−DA. MSC_VIP_PLS−DA was the best model for the classification of oolong tea. The results showed that the use of FHSI could accurately distinguish these four types of oolong tea and was able to identify the key wavelengths affecting the tea classification, which were 650.11, 660.29, 665.39, 675.6, 701.17, 706.31, 742.34 and 747.5 nm. In these wavelengths, different kinds of tea have significant differences (p < 0.05). This study could provide a non-destructive and rapid method for future tea identification.

The Rapid Non-Destructive Detection of Adulteration and Its Degree of Tieguanyin by Fluorescence Hyperspectral Technology

Tieguanyin is one of the top ten most popular teas and the representative of oolong tea in China. In this study, a rapid and non-destructive method is developed to detect adulterated tea and its degree. Benshan is used as the adulterated tea, which is about 0%, 10%, 20%, 30%, 40%, and 50% of the total weight of tea samples, mixed with Tieguanyin. Taking the fluorescence spectra from 475 to 1000 nm, we then established the 2-and 6-class discriminant models. The 2-class discriminant models had the best evaluation index when using SG-CARS-SVM, which can reach a 100.00% overall accuracy, 100.00% specificity, 100% sensitivity, and the least time was 1.2088 s, which can accurately identify pure and adulterated tea; among the 6-class discriminant models (0% (pure Tieguanyin), 10, 20, 30, 40, and 50%), with the increasing difficulty of adulteration, SNV-RF-SVM had the best evaluation index, the highest overall accuracy reached 94.27%, and the least time was 0.00698 s. In general, the results indicated that the two classification methods explored in this study can obtain the best effects. The fluorescence hyperspectral technology has a broad scope and feasibility in the non-destructive detection of adulterated tea and other fields.

Sesame β-ketoacyl-acyl carrier protein synthase I regulates pollen development by interacting with an adenosine triphosphate-binding cassette transporter in transgenic Arabidopsis

Male gametogenesis is an important biological process critical for seed formation and successful breeding. Understanding the molecular mechanisms of male fertility might facilitate hybrid breeding and increase crop yields. Sesame anther development is largely unknown. Here, a sesame β-ketoacyl-[acyl carrier protein] synthase I (SiKASI) was cloned and characterized as being involved in pollen and pollen wall development. Immunohistochemical analysis showed that the spatiotemporal expression of SiKASI protein was altered in sterile sesame anthers compared with fertile anthers. In addition, SiKASI overexpression in Arabidopsis caused male sterility. Cytological observations revealed defective microspore and pollen wall development in SiKASI-overexpressing plants. Aberrant lipid droplets were detected in the tapetal cells of SiKASI-overexpressing plants, and most of the microspores of transgenic plants contained few cytoplasmic inclusions, with irregular pollen wall components embedded on their surfaces. Moreover, the fatty acid metabolism and the expression of a sporopollenin biosynthesis-related gene set were altered in the anthers of SiKASI-overexpressing plants. Additionally, SiKASI interacted with an adenosine triphosphate (ATP)-binding cassette (ABC) transporter. Taken together, our findings suggested that SiKASI was crucial for fatty acid metabolism and might interact with ABCG18 for normal pollen fertility in Arabidopsis.

Fast identification of the geographical origin of Gastrodia elata using excitation-emission matrix fluorescence and chemometric methods

Geographical origin is an important factor affecting the quality of traditional Chinese medicine. In this paper, the identification of geographical origin of Gastrodia elata was performed by using excitation-emission matrix fluorescence and chemometric methods. Firstly, excitation-emission matrix (EEM) fluorescence spectra of Gastrodia elata samples from different geographical origins were obtained. And then three chemometric methods, including multilinear partial least squares discriminant analysis (N-PLS-DA), unfold partial least squares discriminant analysis (U-PLS-DA), and k-nearest neighbor (kNN) method, were applied to build discriminant models. Finally, 45 Gastrodia elata samples could be differentiated from each other by these classification models according to their geographical origins. The results showed that all models obtained good classification results. Compared with the N-PLS-DA and U-PLS-DA, kNN got more accurate and reliable classification results and could identify Gastrodia elata samples from different geographical origins with 100% accuracy on the training and test set. Therefore, the proposed method was available for easily and quickly distinguishing the geographical origin of Gastrodia elata, which can be considered as a promising alternative method for determining the geographic origin of other traditional Chinese medicines.

Characterization of engine lubricants by fluorescence spectroscopy and chemometrics

In this study, principal component analysis (PCA) and parallel factor analysis (PARAFAC) combined with excitation-emission matrix fluorescence (EEMF) were used to determine the most efficient excitation wavelengths of engine lubricants; identify their fluorophores; classify them and look for correlations between their fluorescence and their physical parameters. EEMF spectra were obtained for the different samples in the range of 260 to 600 nm, and 300 to 700 nm for excitation and emission wavelengths respectively. PCA and PARAFAC showed that the efficient excitation wavelengths for engine lubricants are 300, 350, 400, 450 and 470 nm. These five wavelengths represented the maxima of the PARAFAC recovered excitation profiles, of which two were attributed to fluorene and pyrene. The relative proportions of the PARAFAC retrieved components were used to classify engine lubricants with a satisfactory percentage of classification of 70%. Finally, a good correlation was obtained between some physical parameters (particularly the viscosity) of engine lubricants and their fluorescence.

Total synchronous fluorescence spectroscopy coupled with deep learning to rapidly identify the authenticity of sesame oil

The quality of sesame oil (SO) has been paid more and more attention. In this study, total synchronous fluorescence (TSyF) spectroscopy and deep neural networks were utilized to identify counterfeit and adulterated sesame oils. Firstly, typical samples including pure SO, counterfeit sesame oil (CSO) and adulterated sesame oil (ASO) were characterized by TSyF spectra. Secondly, three data augmentation methods were selected to increase the number of spectral data and enhance the robustness of the identification model. Then, five deep network architectures, including Simple Recurrent Neural Network (Simple RNN), Long Short-Term Memory (LSTM) network, Gated Recurrent Unit (GRU) network, Bidirectional LSTM (BLSTM) network and LSTM fortified with Convolutional Neural Network (LSTMC), were designed to identify the CSO and trace the source with 100% accuracy. Finally, ASO samples were also 100% correctly identified by training these network architectures. These results supported the feasibility of the novel method.

Prediction of chemical parameters and authentication of various cold pressed oils with fluorescence and mid-infrared spectroscopic methods

It was aimed to compare the performances of two spectroscopic methods, fluorescence and mid-infrared spectroscopy, in terms of their adulteration detection and estimation of several chemical properties for various cold pressed seed oils. Spectroscopic profiles, fatty acid, free fatty acid and total phenol contents of pumpkin seed, grape seed, black cumin oil, and sesame seed oils were determined and these oils were mixed with sunflower oil at 1-50% (v/v). Both spectroscopic techniques provided comparable results for determination of adulteration of each oil type and the most successful prediction was obtained for pumpkin seed oil at levels >%1. Combined data set of oils resulted in successful quantification of their free fatty acid value, total phenol and major fatty acids contents with both spectroscopic methods regardless of oil type. Both techniques could be used as reliable, fast and environmentally friendly alternatives in the analyses of different types of seed oils.

Tracing the Geographical Origins of Dendrobe (Dendrobium spp.) by Near-Infrared Spectroscopy Sensor Combined with Porphyrin and Chemometrics

Dendrobe (Dendrobium spp.) is a traditional medicinal and edible food, which is rich in nutrients and contains biologically active metabolites. The quality and price of dendrobe are related to its geographical origins, and high quality dendrobe is often imitated by low quality dendrobe in the market. In this work, near-infrared (NIR) spectroscopy sensor combined with porphyrin and chemometrics was used to distinguish 360 dendrobe samples from twelve different geographical origins. Partial least squares discriminant analysis (PLSDA) was used to study the sensing performance of traditional NIR and tera-(4-methoxyphenyl)-porphyrin (TMPP)-NIR on the identification of dendrobe origin. In the PLSDA model, the recognition rate of the training and prediction set of the TMPP-NIR could reach 100%, which was higher than the 91.85% and 91.34% of traditional NIR. And the accuracy, sensitivity, and specificity of the TMPP-NIR sensor are all 1.00. The mechanism of TMPP improving the specificity of NIR spectroscopy should be related to the π-π conjugated system and the methoxy groups of TMPP interact with the chemical components of dendrobe. This study reflected that NIR spectrum with TMPP sensor was an effective approach for identifying the geographic origin of dendrobe.