Comparison of IRMS, GC-MS and E-Nose data for the discrimination of saffron samples with different origin, process and age

Characterization of Aroma Composition of Amomum tsaoko During the Drying Process Based on GC-MS

Amomum tsaoko is an important spice and medicinal plant widely utilized in East and Southeast Asia. Non-targeted metabolomics techniques were employed to study the variations in the content and composition of essential oil from A. tsaoko during drying at different temperatures: 40°C, 50°C, 60°C, and 70°C. A total of 260 metabolites were detected using gas chromatography-mass spectrometry (GC-MS), mainly terpenoids and aldehydes. Cineole, the most important component, accumulated abundantly in samples dried at 50°C. A higher temperature (70°C) was conducive to the accumulation of aldehydes. Overall, the optimal drying condition for A. tsaoko was determined to be 50°C for 50 h. In addition, nine differential metabolites were screened using variable important in projection and p value (VIP > 1 and p < 0.05), which may serve as potential flavor markers to differentiate various drying treatments of A. tsaoko. This study provides a novel perspective on understanding the dynamic metabolites changes during the drying process, and establishes a theoretical foundation for the refinement and high-quality processing of A. tsaoko.

Characterization of saffron from different origins by HS-GC-IMS and authenticity identification combined with deep learning

With the rising demand of saffron, it is essential to standardize the confirmation of its origin and identify any adulteration to maintain a good quality led market product. However, a rapid and reliable strategy for identifying the adulteration saffron is still lacks. Herein, a combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and convolutional neural network (CNN) was developed. Sixty-nine volatile compounds (VOCs) including 7 groups of isomers were detected rapidly and directly. A CNN prediction model based on GC-IMS data was proposed. With the merit of minimal data prepossessing and automatic feature extraction capability, GC-IMS images were directly input to the CNN model. The origin prediction results were output with the average accuracy about 90 %, which was higher than traditional methods like PCA (61 %) and SVM (71 %). This established CNN also showed ability in identifying counterfeit saffron with a high accuracy of 98 %, which can be used to authenticate saffron.

Influence of Solvent Polarity on Crocin Content and Surface Properties of Saffron (Crocus sativus L.) Extracts

The saffron composition is being widely studied for authenticity and traceability, but very few works have been carried out to investigate the relationship between the chemical and physico-chemical properties of saffron solutes and their technological functionality in colloidal systems. This study aims at evaluating the surface properties of saffron extracts obtained using solvents of different polarities to achieve extracts with different compositions in terms of the pattern and content of polar and medium polarity crocins. The air-water surface was evaluated alone and in the presence of Tween 20 at different surfactant-extract ratios. Saffron extracts were able to decrease the surface tension of the aqueous phase, indicating the presence of surface-active compounds. In the mixed saffron extract-Tween 20 systems, competitive adsorption at the air-water interface occurred when the surfactant was present at a low concentration, while at concentrations higher than the CMC, Tween 20 hindered the adsorption of the extract surface-active compounds. The results highlight the interesting technological functionality of saffron extracts for applications in colloidal systems. To better exploit their use in the design and development of formulated foods, nutraceutics and pharma products, further studies are needed to unravel the relationship between the composition of saffron extracts and corresponding surface activity.

Geographical traceability of flavor compounds in Chinese mitten crab (Eriocheir sinensis): Implications for quality differentiation, authenticity assessment, and mechanism research

Geographical traceability plays a crucial role in ensuring quality assurance, brand establishment, and the sustainable development of the crab industry. In this study, we examined the possibility of using gas chromatography-ion mobility spectrometry with multivariate statistical authenticity analysis to identify the origin of crabs from five sites downstream of the Yangtze River. Significant variations were observed in the levels of alcoholic flavor compounds in the hepatopancreas and muscles of crabs from different geographical locations, and a support vector machine exhibited discriminant ability with 100% accuracy. These flavor variations exhibited significant correlations with the types and concentrations of elements within the crabs, as well as with free amino acids. This study offers a practical approach for determining the geographical traceability of Chinese mitten crabs and elucidates the role of elements in flavor modulation, thereby providing innovative strategies to enhance the efficiency of crab farming.

Elemental composition and contaminants of saffron from different origins and geographical discrimination using chemometrics

Elements such as As, Cd, Cr and Pb are classified as contaminants of major concern for public health, due to their high degree of toxicity. Saffron is an important medicinal herbal spice used in variety of food items, pharmaceutical medicines, and cosmetics. Presence of heavy metals in saffron will increase the health risk to consumers. Also, authentication of geographical origin of saffron is an issue of utmost importance for global trading. The present study is focused on investigation of elemental contaminants in saffron and elemental composition of saffron from India (Jammu and Kashmir); Iran and Afghanistan are also explored for geographical discrimination, using Chemometrics. In total, 29 elements including Ag, Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Sb, Se, Si, Sr, Ti, Tl, V and Zn were analyzed using ICP-OES. Toxic elemental contaminants including As, Cd, Pb were found below the maximum permissible limit. Using PCA, elements B, Ni, Ba, Fe, V, Si, Al, Ti, K, Na, Sr, and Zn were found as significant discriminators of geographical origin. Elemental composition of saffron may be utilized, to prevent cases of falsified geographical origin in trade.

Phytochemistry, quality control and medicinal uses of Saffron (Crocus sativus L.): an updated review

Saffron, botanically known as Crocus sativus L., is renowned as the world's most expensive spice and has been utilized in various fields since ancient times. Extensive scientific research has been conducted on Crocus sativus (C. sativus), focusing on its phytochemical composition, diverse applications, and biological activities. C. sativus phytochemicals consist mainly of three compounds, namely crocin, picrocrocin, and safranal, which are responsible for most of its properties. Saffron is rich in bioactive compounds, more than 150 of which have been isolated. Owing to its unique composition and properties, saffron is used in various fields, such as the food industry, perfumery, cosmetics, pharmaceutics, and medicine. However, the high economic value of saffron makes it susceptible to adulteration and various fraudulent practices. To deal with this issue, a number of methods and techniques have been developed to authenticate and determine adulterants in saffron. This paper presents a bibliometric study of saffron based on the Web of Science database, analyzing 3,735 studies published between 2000 and 2021. The study also examined author participation and collaboration networks among countries. Production, transformation, chemical composition, methods of adulteration detection, uses, and health properties of saffron are also discussed.

Detection of Saffron’s Main Bioactive Compounds and Their Relationship with Commercial Quality

This review aims to evaluate the state of saffron’s main bioactive compounds and their relationship with its commercial quality. Saffron is the commercial name for the dried red stigmas of the Crocus sativus L. flower. It owes its sensory and functional properties mainly to the presence of its carotenoid derivatives, synthesized throughout flowering and also during the whole production process. These compounds include crocin, crocetin, picrocrocin, and safranal, which are bioactive metabolites. Saffron’s commercial value is determined according to the ISO/TS3632 standard that determines their main apocatotenoids. Other techniques such as chromatography (gas and liquid) are used to detect the apocarotenoids. This, together with the determination of spectral fingerprinting or chemo typing are essential for saffron identification. The determination of the specific chemical markers coupled with chemometric methods favors the discrimination of adulterated samples, possible plants, or adulterating compounds and even the concentrations at which these are obtained. Chemical characterization and concentration of various compounds could be affected by saffron’s geographical origin and harvest/postharvest characteristics. The large number of chemical compounds found in the by-products (flower parts) of saffron (catechin, quercetin, delphinidin, etc.) make it an interesting aromatic spice as a colorant, antioxidant, and source of phytochemicals, which can also bring additional economic value to the most expensive aromatic species in the world.

Volatile Olfactory Profiles of Umbrian Extra Virgin Olive Oils and Their Discrimination through MOX Chemical Sensors

Extra virgin olive oil (EVOO) is the best vegetable oil worldwide but, at the same time, is one of the product victims of fraud in the agri-food sector, and the differences about quality within the extra-virgin olive oil category are often missed. Several scientific techniques were applied in order to guarantee the authenticity and quality of this EVOO. In the present study, the volatile compounds (VOCs) by gas chromatography-mass spectrometry with solid-phase micro-extraction detection (GC-MS SPME), organoleptic analysis by the official Slow Food panel and the detection by a Small Sensor System (S3) were applied. Ten EVOOs from Umbria, a central Italian region, were selected from the 2021 Slow Food Italian extra virgin olive oil official guide, which includes hundreds of high-quality olive oils. The results demonstrated the possibility to discriminate the ten EVOOs, even if they belong to the same Italian region, by all three techniques. The result of GC-MS SPME detection was comparable at the discrimination level to the organoleptic test with few exceptions, while the S3 was able to better separate some EVOOs, which were not discriminated perfectly by the other two methods. The correlation analysis performed among and between the three methodologies allowed us to identify 388 strong associations with a p value less than 0.05. This study has highlighted how much the mix of VOCs was different even among few and localized EVOOs. The correlation with the sensor detection, which is faster and chipper compared to the other two techniques, elucidated the similarities and discrepancies between the applied methods.

Volatile fingerprints of different parts of Chongming saffron (Crocus sativus) flowers by headspace-gas chromatography-ion mobility spectrometry and in vitro bioactive properties of the saffron tepals

In this work, the volatile fingerprints of different parts of Chongming saffron flowers (stigmas, stamens, and tepals) were analyzed and compared for the first time by headspace-gas chromatography-ion mobility spectrometry. Three different parts of saffron flowers could be clearly distinguished using principal component analysis based on signal intensity data of gas chromatography-ion mobility spectrometry. Therefore, gas chromatography-ion mobility spectrometry coupled with principal component analysis method could be employed as a new method for authentication and quality control of saffron for the reason of frequent addition with stamens and/or tepals as adulterants in saffron. Moreover, the bioactive composition (total flavonoids, total phenolics, and total anthocyanins) and bioactive properties of saffron tepals were evaluated. The results indicated that aqueous, ethanol, and ethyl acetate extracts of saffron tepals exhibited good radical scavenging (2,2-Diphenyl-1-picrylhydrazyl, ABTS, and OH) and enzyme (α-amylase/α-glucosidase) inhibition activities, which probably were attributed to the bioactive components contained in the extracts. This approach would provide the important information for monitoring the quality of saffron as well as exploring the utilization of saffron tepals in functional food technology. PRACTICAL APPLICATION: This study demonstrated that the HS-GC-IMS method might be used as a new strategy for quality control of saffron, and the saffron tepals were rich source of bioactive components that could be used in health-promoting products.

Applications of ultrasound techniques in tandem with non-destructive approaches for the quality evaluation of edible oils

Edible oils include triglycerides that are extracted from oil seeds or fruits such as sunflowers, palms, olives, soys, rapeseeds, cocoa and many others. They are the elementary origins of unsaturated fats and vitamins especially vitamin 'E' in people's diets. Edible oils are at risk of intentional (such as inadequate storage conditions) and unintentional adulteration, so it is necessary to pay attention to their safety, health and fraud. Generally, this evaluation can be destructive or non-destructive. There are numerous methods to evaluate quality of edible oils which include sensory analysis, chemical analysis, chromatography, ultrasound, etc. The Ultrasonic approach is a non-destructive way and also fast, accurate, inexpensive, repeatable, portable and simple. Combination of ultrasound with other techniques such as electronic nose, electronic tongue, visible spectroscopic fingerprints, chemical descriptors, Raman spectroscopy, mid-infrared and machine vision, will improve quality evaluation and detection accuracy. This review summarizes the ultrasound idea and the latest knowledge of its application with other techniques on evaluation of edible oils.

Comparison of Different Drying Methods on the Volatile Components of Ginger (Zingiber officinale Roscoe) by HS-GC-MS Coupled with Fast GC E-Nose

Ginger (Zingiber officinale Roscoe) is one of the most popular spices in the world, with its unique odor. Due to its health benefits, ginger is also widely used as a dietary supplement and herbal medicine. In this study, the main flavor components of gingers processed by different drying methods including hot air drying, vacuum drying, sun-drying, and vacuum-freeze drying, were identified on the basis of headspace-gas chromatography coupled with mass spectrometry (HS-GC-MS) and fast gas chromatography electronic-nose (fast GC e-nose) techniques. The results showed that the ginger dried by hot air drying exhibited high contents of volatile compounds and retained the richest odor in comparison with those dried by other methods, which indicated that hot air drying is more suitable for the production of dried ginger. Sensory description by fast GC e-nose exhibited that ginger flavor was mainly concentrated in the spicy, sweet, minty, fruity, and herbaceous odor. The relative content of the zingiberene was significantly higher in the hot air drying sample than those by other methods, suggesting that dried ginger by hot air drying can retain more unique spicy and pungent odorants. Furthermore, the results of chemometrics analyses showed that the main variance components among the samples by different drying methods were α-naginatene, (+)-cyclosativene, and sulcatone in HS-GC-MS analysis, and α-terpinen-7-al, dimethyl sulfide, and citronellal in fast GC e-nose analysis. For comparison of fresh and dried gingers, terpinolene, terpinen-4-ol, 2,4-decadienal, (E, Z)-, and linalool were considered the main variance components. This study generated a better understanding of the flavor characteristics of gingers by different drying methods and could provide a guide for drying and processing of ginger.

Recent techniques for the authentication of the geographical origin of tea leaves from camellia sinensis: A review

Tea is one of the most important beverages worldwide, is produced in several distinct geographical regions, and is traded on the global market. The ability to determine the geographical origin of tea products helps to ensure authenticity and traceability. This paper reviews the recent research on authentication of tea using a combination of instrumental and chemometric methods. To determine the production region of a tea sample, instrumental methods based on analyzing isotope and mineral element contents are suitable because they are less affected by tea variety and processing methods. Chemometric analysis has proven to be a valuable method to identify tea. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most preferred methods for processing large amounts of data obtained through instrumental component analysis.

Multivariate Statistical Analysis Uncovers Spectrum–Effect Relationship between HPLC Fingerprints and Antioxidant Activity of Saffron

Crocus sativus L. is commonly used as functional food and medicinal herb in traditional Chinese medicine. In this study, the spectrum–effect relationship was established between HPLC fingerprints and in vitro antioxidant activity of saffron to improve the quality evaluation method of saffron. The fingerprints of 21 batches of saffron collected from different regions were assessed, and the data were further analyzed by chemometric methods, including similarity analysis, hierarchical clustering analysis, principal component analysis, and orthogonal partial least squares discriminant analysis. The spectrum–effect relationship between fingerprints and antioxidant effect of saffron was analyzed by grey relational analysis and partial least square methods to figure out the antioxidant component of saffron. Thirteen common peaks of 21 batches of saffron were included in the analysis, and peak 3 (picrocrocin), peak 7 (crocin I), and peak 10 (crocin II) were identified as the main active components responsible for antioxidant efficacy. Besides, a multi-index quality control method was developed for simultaneous determination of these three antioxidant components in saffron. Taken together, this study provided new strategies for the quality control and the development of new bioactive products of saffron in the future.

The Relation between Drying Conditions and the Development of Volatile Compounds in Saffron (Crocus sativus)

Saffron is derived from the stigmas of the flower Crocus sativus L. The drying process is the most important post-harvest step for converting C. sativus stigmas into saffron. The aim of this review is to evaluate saffron's post-harvest conditions in the development of volatile compounds and its aroma descriptors. It describes saffron's compound generation by enzymatic pathways and degradation reactions. Saffron quality is described by their metabolite's solubility and the determination of picrocrocin, crocins, and safranal. The drying process induce various modifications in terms of color, flavor and aroma, which take place in the spice. It affects the aromatic species chemical profile. In the food industry, saffron is employed for its sensory attributes, such as coloring, related mainly to crocins (mono-glycosyl esters or di-glycosyl polyene).

Metabolomics Approaches for the Comprehensive Evaluation of Fermented Foods: A Review

Fermentation is an important process that can provide new flavors and nutritional and functional foods, to deal with changing consumer preferences. Fermented foods have complex chemical components that can modulate unique qualitative properties. Consequently, monitoring the small molecular metabolites in fermented food is critical to clarify its qualitative properties and help deliver personalized nutrition. In recent years, the application of metabolomics to nutrition research of fermented foods has expanded. In this review, we examine the application of metabolomics technologies in food, with a primary focus on the different analytical approaches suitable for food metabolomics and discuss the advantages and disadvantages of these approaches. In addition, we summarize emerging studies applying metabolomics in the comprehensive analysis of the flavor, nutrition, function, and safety of fermented foods, as well as emphasize the applicability of metabolomics in characterizing the qualitative properties of fermented foods.

1H-NMR Metabolic Profiling and Antioxidant Activity of Saffron (Crocus sativus) Cultivated in Lebanon

Despite the beneficial health properties shown by Lebanese saffron, its qualitative and quantitative composition has never been investigated before. In the present study, NMR spectroscopy, together with antioxidant activity assays, were applied to evaluate the chemical composition of saffron samples of different geographical origins (Lebanon, Italy, Iran, and India) and to categorize the Lebanese saffron for the first time. The distinction between Lebanese saffron and that produced in other countries was attributed to its higher linolenic and linoleic fatty acids, glucose and picrocrocin contents. Moreover, spices produced in three different regions of the Lebanese territory have been clearly differentiated. Saffron cultivated in the Qaa region displayed a high glucose, fatty acids and polyphenols content, whereas Hermel saffron exhibited the largest rate of picrocrocin and glycosylated carotenoids. Finally, samples from Baalbeck showed lower rates for the majority of metabolites. Moreover, Lebanese saffron showed a high antioxidant activity in ABTS and DPPH assays. A low dose of saffron extract (10 µg/mL) inhibited the growth of human lung adenocarcinoma cells, probably due to the high polyphenolic content. This study highlights the quality and peculiarity of Lebanese saffron cultivated in Northern Beqaa district and allows for a good discrimination between spices produced in relatively close territory.

Geographical origin identification of two salmonid species via flavor compound analysis using headspace-gas chromatography-ion mobility spectrometry combined with electronic nose and tongue

The flavor of salmonids is affected by species and origin. Sources of salmonid fish fillets are complex and difficult to identify and label fraud occasionally occurs in the market. In this study, headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS), electronic nose, electronic tongue and amino acid detection technologies were used to analyze flavor compounds in two salmonid species from different geographical origins. Fingerprints of volatile compounds of salmonid were constructed using HS-GC-IMS technology. Free amino acid (FAA) content differed in salmonids from different geographical origins. Regarding salmonid odor, HS-GC-IMS analysis results were basically consistent with those of the electronic nose. Regarding taste, the conclusions drawn from the electronic tongue were consistent with the amino acid test results. Therefore, our results demonstrate that flavor compounds can be used to distinguish salmonids from different geographical origins, providing a new dimension to food safety and authenticity. Furthermore, HS-GC-IMS, electronic nose and tongue can be used as tools in the market to identify food fraud.

Analysis of aroma-active volatiles in an SDE extract of white tea

White tea is a famous Chinese tea that is cooked at boiling point before drinking. The simultaneous distillation-extraction (SDE) was used to collect volatile compounds during tea cooking. The SDE extract was dominated with green, floral, roasted and woody notes, and weak sweet note. There were 32 volatile compounds identified via gas chromatography-mass spectrometry analysis, and 19 of them had strong fragrance based on the gas chromatography-olfactometry analyzed results. Hexanal, 2-hexenal, cis-3-hexen-1-ol, and camphene were the main contributors to the green note. The floral note was mainly contributed by 2-hexanone, benzeneacetaldehyde, trans-linalool oxide, and linalool, and the sweet note was induced by trans-β-damascenone. The roasted note was mainly contributed by 2-pentyl-furan. The woody note was mainly contributed by trans-α-ionone and trans-β-ionone. Four putative reaction pathways, including amino acid degradation, carotene degradation, Maillard reaction, and glycosides hydrolysis, were figured out to explain the generation of aromatic-active volatiles at high temperatures. This study added our knowledge on tea aroma under cooking as well as other thermal treatments.

A hybrid electronic nose system for discrimination of pathogenic bacterial volatile compounds

A hybrid electronic nose comprising an array of three organic-inorganic nanocomposite gas sensors [zinc tetra tert-butyl phthalocyanine (ZnTTBPc), zinc tetra-phenyl porphyrin (ZnTPP), and cobalt tetraphenyl-porphyrin (CoTPP)] coupled with three commercial metal-oxide semiconductor gas sensors (TGS 2444, TGS 2603 and TGS 2620) was developed to discriminate bacterial volatile compounds. Each type of gas sensor had its own strengths and weaknesses in terms of its capability to detect complex odors from the five different bacterial species tested. Bacterial samples were controlled at a fixed initial bacterial concentration by measuring the optical density at 600 nm of the culture suspensions. A comparative evaluation of the volatile compound fingerprints from five bacterial species grown in Luria-Bertani medium was conducted to identify the optimal incubation time for detection of volatile biomarkers to discriminate among bacteria. The results suggest that the hybrid electronic nose was indeed able to discriminate among the bacterial species and culture media, with a variance based on contributions of 92.4% from PC1 and 7.2% from PC2, at an incubation time of 6 hours. Furthermore, the results of hierarchical cluster analysis showed that bacterial odor data formed two major bacterial groups, with the maximum cluster distance close to 25. Intra-group similarity was demonstrated as the two bacterial species (E. cloacae and P. aeruginosa) from among the Gram-negative bacteria had a greater similarity with a cluster distance close to 4. Finally, the minimum distance between E. cloacae and S. Typhi was approximately 1, at an equal distance from E. coli and S. aureus.

Aroma dynamic characteristics during the process of variable-temperature final firing of Congou black tea by electronic nose and comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry

The drying technology is crucial to the quality of Congou black tea. In this study, the aroma dynamic characteristics during the variable-temperature final firing of Congou black tea was investigated by electronic nose (e-nose) and comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC × GC-TOFMS). Varying drying temperatures and time obtained distinctly different types of aroma characteristics such as faint scent, floral aroma, and sweet fragrance. GC × GC-TOFMS identified a total of 243 volatile compounds. Clear discrimination among different variable-temperature final firing samples was achieved by using partial least squares discriminant analysis (R2Y = 0.95, Q2 = 0.727). Based on a dual criterion of variable importance in the projection value (VIP > 1.0) and one-way ANOVA (p < 0.05), ninety-one specific volatile biomarkers were identified, including 2,6-dimethyl-2,6-octadiene and 2,5-diethylpyrazine with VIP > 1.5. In addition, for the overall odor perception, e-nose was able to distinguish the subtle difference during the variable-temperature final firing process.

Peptides, DNA and MIPs in Gas Sensing. From the Realization of the Sensors to Sample Analysis

Detection and monitoring of volatiles is a challenging and fascinating issue in environmental analysis, agriculture and food quality, process control in industry, as well as in 'point of care' diagnostics. Gas chromatographic approaches remain the reference method for the analysis of volatile organic compounds (VOCs); however, gas sensors (GSs), with their advantages of low cost and no or very little sample preparation, have become a reality. Gas sensors can be used singularly or in array format (e.g., e-noses); coupling data output with multivariate statical treatment allows un-target analysis of samples headspace. Within this frame, the use of new binding elements as recognition/interaction elements in gas sensing is a challenging hot-topic that allowed unexpected advancement. In this review, the latest development of gas sensors and gas sensor arrays, realized using peptides, molecularly imprinted polymers and DNA is reported. This work is focused on the description of the strategies used for the GSs development, the sensing elements function, the sensors array set-up, and the application in real cases.

Cognitive spectroscopy for evaluating Chinese black tea grades (Camellia sinensis): near-infrared spectroscopy and evolutionary algorithms

BACKGROUND

Grading represents an essential criterion for the quality assurance of black tea. The main objectives of the study were to develop a highly robust model for Chinese black tea of seven grades based on cognitive spectroscopy.

RESULTS

Cognitive spectroscopy was proposed to combine near-infrared spectroscopy (NIRS) with machine learning and evolutionary algorithms, selected feature information from complex spectral data and show the best results without human intervention. The NIRS measuring system was used to obtain the spectra of Chinese black tea samples of seven grades. The spectra acquired were preprocessed by standard normal variate transformation (SNV), multiplicative scatter correction (MSC) and minimum/maximum normalization (MIN/MAX), and the optimal pretreating method was implemented using principal component analysis combined with linear discriminant analysis algorithm. Three feature selection evolutionary algorithms, which were a genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO), were compared to search the best preprocessed characteristic wavelengths. Cognitive models of Chinese black tea ranks were constructed using extreme learning machine (ELM), K-nearest neighbor (KNN) and support vector machine (SVM) methods based on the selected characteristic variables. Experimental results revealed that the PSO-SVM model showed the best predictive performance with the correlation coefficients of prediction set (R_p ) of 0.9838, the root mean square error of prediction (RMSEP) of 0.0246, and the correct discriminant rate (CDR) of 98.70%. The extracted feature wavelengths were only occupying 0.18% of the origin.

CONCLUSION

The overall results demonstrated that cognitive spectroscopy could be utilized as a rapid strategy to identify Chinese black tea grades. © 2020 Society of Chemical Industry.

Geographical Classification of Italian Saffron (Crocus sativus L.) by Multi-Block Treatments of UV-Vis and IR Spectroscopic Data

One-hundred and fourteen samples of saffron harvested in four different Italian areas (three in Central Italy and one in the South) were investigated by IR and UV-Vis spectroscopies. Two different multi-block strategies, Sequential and Orthogonalized Partial Least Squares Linear Discriminant Analysis (SO-PLS-LDA) and Sequential and Orthogonalized Covariance Selection Linear Discriminant Analysis (SO-CovSel-LDA), were used to simultaneously handle the two data blocks and classify samples according to their geographical origin. Both multi-block approaches provided very satisfying results. Each model was investigated in order to understand which spectral variables contribute the most to the discrimination of samples, i.e., to the characterization of saffron harvested in the four different areas. The most accurate solution was provided by SO-PLS-LDA, which only misclassified three test samples over 31 (in external validation).

Highly identification of keemun black tea rank based on cognitive spectroscopy: Near infrared spectroscopy combined with feature variable selection

From the perspective of combating fraud issues and examining keemun black tea properties, there was a contemporary urgent demand for a keemun black tea rankings identification system. Current rapid evaluation systems had been mainly developed for green tea grade evaluation, but there was space for improvement to establish a highly robust model. The present study proposed cognitive spectroscopy that combined near infrared spectroscopy (NIRS) with multivariate calibration and feature variable selection methods. We defined "cognitive spectroscopy" as a protocol that selects characteristic information from complex spectral data and showed optimal results without human intervention. 700 samples representing keemun black tea from seven quality levels were scanned applying an NIR sensor. To differentiate which wavelength variables of the acquired NIRS data carry key and feature information regarding keemun black tea grades, there were four different variables screening approaches, namely genetic algorithm (GA), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and shuffled frog leaping algorithm (SFLA), were compared in this study. Cognitive models were developed using least squares support vector machine (LSSVM), back propagation neural network (BPNN) and random forest (RF) methods combined with the optimized characteristic variables from the above variables selection algorithms for the identification of keemun black tea rank quality. Experimental results showed that all cognitive models utilizing the SFLA approach achieved steady predictive results based on eight latent variables and selected thirteen characteristic wavelength variables. The CARS-LSSVM model with the best predictive performance was proposed based on selecting ten characteristic latent variables, and the best performance indicators of the model were as follows: the root mean square error of prediction (RMSEP) was 0.0413, the correlation coefficients of prediction set (R_p) was 0.9884, and the correct discriminant rate (CDR) was 99.01% in the validation process. This study demonstrated that cognitive spectroscopy represented a proper strategy for the highly identification of quality rankings of keemun black tea.

Nanomaterial Gas Sensors for Online Monitoring System of Fruit Jams

Jams are appreciated worldwide and have become a growing market, due to the greater attention paid by consumers for healthy food. The selected products for this study represent a segment of the European market that addresses natural products without added sucrose or with a low content of natural sugars. This study aims to identify volatile organic compounds (VOCs) that characterize three flavors of fruit and five recipes using gas chromatography-mass spectrometry (GC-MS) and solid-phase micro-extraction (SPME) analysis. Furthermore, an innovative device, a small sensor system (S3), based on gas sensors with nanomaterials has been used; it may be particularly advantageous in the production line. Results obtained with linear discriminant analysis (LDA) show that S3 can distinguish among the different recipes thanks to the differences in the VOCs that are present in the specimens, as evidenced by the GC-MS analysis. Finally, this study highlights how the thermal processes for obtaining the jam do not alter the natural properties of the fruit.

Identification of Cannabis sativa L. (hemp) Retailers by Means of Multivariate Analysis of Cannabinoids

In this work, the concentration of nine cannabinoids, six neutral cannabinoids (THC, CBD, CBC, CBG, CBN and CBDV) and three acidic cannabinoids (THCA CBGA and CBDA), was used to identify the Italian retailers of Cannabis sativa L. (hemp), reinforcing the idea that the practice of categorizing hemp samples only using THC and CBD is inadequate. A high-performance liquid chromatography/high-resolution mass spectrometry (HPLC-MS/MS) method was developed for screening and simultaneously analyzing the nine cannabinoids in 161 hemp samples sold by four retailers located in different Italian cities. The hemp samples dataset was analyzed by univariate and multivariate analysis with the aim to identify the hemp retailers without any other information on the hemp samples like Cannabis strains, seeds, soil and cultivation characteristics, geographical origin, product storage, etc. The univariate analysis highlighted that the hemp samples could not be differentiated by using any of the nine cannabinoids analyzed. To evaluate the real efficiency of the discrimination among the four hemp retailers a partial least squares discriminant analysis (PLS-DA) was applied. The PLS-DA results showed a very good discrimination between the four hemp retailers with an explained variance of 100% and low classification errors in both calibration (5%) and cross validation (6%). A total of 92% of the hemp samples were correctly classified by the cannabinoid variables in both fitting and cross validation. This work contributed to show that an analytical method coupled with multivariate analysis can be used as a powerful tool for forensic purposes.