Quality assessment of olive oils based on temperature-ramped HS-GC-IMS and sensory evaluation: Comparison of different processing approaches by LDA, kNN, and SVM

Characterization and exploration of dynamic variation of volatile compounds in vine tea during processing by GC-IMS and HS-SPME/GC-MS combined with machine learning algorithm

It is imperative to unravel the dynamic variation of volatile components of vine tea during processing to provide guidance for tea quality evaluation. In this study, the dynamic changes of volatile compounds of vine tea during processing were characterized by GC-IMS and HS-SPME/GC-MS. As a result, 103 volatile compounds were characterized by the two technologies with three overlapped ones. The random forest approach was employed to develop the models and explore key volatile compounds. 23 key compounds were explored, among which 13 were derived from GC-IMS and ten were from HS-SPME/GC-MS. Moreover, the area under the receiver operating characteristics curve with 100 cross validations by the pair-wised models were all 1 for the established models. Furthermore, the primary aroma formation mechanism for the key volatile compounds were mainly involved in fatty acid and amino acid metabolism. Besides, this study provides a theoretical support for directed processing and quality control of vine tea.

Advanced analytical techniques for authenticity identification and quality evaluation in Essential oils: A review

Essential oils (EO), secondary metabolites of plants are fragrant oily liquids with antibacterial, antiviral, anti-inflammatory, anti-allergic, and antioxidant effects. They are widely applied in food, medicine, cosmetics, and other fields. However, the quality of EOs remain uncertain owing to their high volatility and susceptibility to oxidation, influenced by factors such as the harvesting season, extraction, and separation techniques. Additionally, the huge economic value of EOs has led to a market marked by widespread and varied adulteration, making the assessment of their quality challenging. Therefore, developing simple, quick, and effective identification techniques for EOs is essential. This review comprehensively summarizes the techniques for assessing EO quality and identifying adulteration. It covers sensory evaluation, physical and chemical property evaluation, and chemical composition analysis, which are widely used and of great significance for the quality evaluation and adulteration detection of EOs.

Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array

As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

Nanotechnology-based optical sensors for Baijiu quality and safety control

Baijiu quality and safety have received considerable attention owing to the gradual increase in its consumption. However, owing to the unique and complex process of Baijiu production, issues leading to quality and safety concerns may occur during the manufacturing process. Therefore, establishing appropriate analytical methods is necessary for Baijiu quality assurance and process control. Nanomaterial (NM)-based optical sensing techniques have garnered widespread interest because of their unique advantages. However, comprehensive studies on nano-optical sensing technology for quality and safety control of Baijiu are lacking. In this review, we systematically summarize NM-based optical sensor applications for the accurate detection and quantification of analytes closely related to Baijiu quality and safety. Furthermore, we evaluate the sensing mechanisms for each application. Finally, we discuss the challenges nanotechnology poses for Baijiu analysis and future trends. Overall, nanotechnological approaches provide a potentially useful alternative for simplifying Baijiu analysis and improving final product quality and safety.

Identification of Pancreatic Metastasis Cells and Cell Spheroids by the Organelle-Targeting Sensor Array

Pancreatic cancer is a highly malignant and metastatic cancer. Pancreatic cancer can lead to liver metastases, gallbladder metastases, and duodenum metastases. The identification of pancreatic cancer cells is essential for the diagnosis of metastatic cancer and exploration of carcinoma in situ. Organelles play an important role in maintaining the function of cells, the various cells show significant differences in organelle microenvironment. Herein, six probes are synthesized for targeting mitochondria, lysosomes, cell membranes, endoplasmic reticulum, Golgi apparatus, and lipid droplets. The six fluorescent probes form an organelles-targeted sensor array (OT-SA) to image pancreatic metastatic cancer cells and cell spheroids. The homology of metastatic cancer cells brings the challenge for identification of these cells. The residual network (ResNet) model has been proven to automatically extract and select image features, which can figure out a subtle difference among similar samples. Hence, OT-SA is developed to identify pancreatic metastasis cells and cell spheroids in combination with ResNet analysis. The identification accuracy for the pancreatic metastasis cells (> 99%) and pancreatic metastasis cell spheroids (> 99%) in the test set is successfully achieved respectively. The organelles-targeting sensor array provides a method for the identification of pancreatic cancer metastasis in cells and cell spheroids.

Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma

Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis and treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital for advancing diagnostics and therapies. This study combines bioinformatics and machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such as Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, and survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with HCC's pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, and ESR1 were significantly positively correlated with overall patient survival, while AKR1B10 and NQO1 displayed a negative correlation. Moreover, the Adaboost prediction model yielded an 86.8 % accuracy, showcasing machine learning's potential in deciphering complex dataset patterns for clinically relevant predictions. These findings promise to contribute valuable insights into the elusive mechanisms driving liver cancer (HCC). They hold the potential to guide the development of more precise diagnostic methods and treatment strategies in the future. In the fight against this global health challenge, unraveling HCC's intricacies is of paramount importance.

Paper spray mass spectrometry profiling of olive oil unsaponifiable fraction for commercial categories classification

A new method for a fast molecular profiling of olive oil unsaponifiable fraction has been developed. This approach, based on paper spray mass spectrometry, allows obtaining MS data with only a few minutes of analysis and without significant solvent and disposable consumption. Tandem mass spectrometry and high-resolution mass spectrometry experiments have been performed to identify the main ions detected. The MS data coming from the analyses of sixty-three samples of three different olive oil categories: extra virgin olive oil (EVOO), virgin olive oil (VOO), and pomace olive oil (POO), have been used to test the discriminative potential. Both unsupervised (PCA and HCA) and supervised (kNN and LDA) chemometric procedures have been applied with good results in prediction. The same approach was tested using direct infusion mass spectrometry data to confirm the ability of paper spray fingerprinting to classify different olive oils correctly.

Stable SERS Detection of Lactobacillus fermentum Using Optical Tweezers in a Microfluidic Environment

Rapid identification of fermented lactic acid bacteria has long been a challenge in the brewing industry. This study combined label-free surface-enhanced Raman scattering (SERS) and optical tweezer technology to construct a test platform within a microfluidic environment. Six kinds of lactic acid bacteria common in industry were tested to prove the stability of the SERS spectra. The results demonstrated that the utilization of optical tweezers to securely hold the bacteria significantly enhanced the stability of the SERS spectra. Furthermore, SVM and XGBoost machine learning algorithms were utilized to analyze the obtained Raman spectra for identification, and the identification accuracies exceeded 95% for all tested lactic acid bacteria. The findings of this study highlight the crucial role of optical tweezers in improving the stability of SERS spectra by capturing bacteria in a microfluidic environment, prove that this technology could be used in the rapid identification of lactic acid bacteria, and show great significance in expanding the applicability of the SERS technique for other bacterial testing purposes.

Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice

Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.

Comparing the potential of IR-spectroscopic techniques to gas chromatography coupled to ion mobility spectrometry for classifying virgin olive oil categories

Virgin olive oil (OO) can be classified into three different categories: extra virgin, virgin and lampante. The official method for this classification, based on physicochemical analysis and sensory tasting, is considered useful and effective, although it is a costly and time-consuming process. The aim of this study was to assess the potential of some analytical techniques for classifying and predicting different OO categories to support official methods and to provide olive oil companies with a rapid tool to assess product quality. Thus, mid and near infrared spectroscopies (MIR and NIR) have been compared by using different instruments and with head-space gas chromatography coupled to an ion mobility spectrometer (HS-GC-IMS). High classification success rates in validation models were obtained using IR spectrometers (>70% and > 80% in average for ternary and binary classifications, respectively), although HS-GC-IMS showed greater classification potential (>85% and > 90%).

A methodological approach to preprocessing FTIR spectra of adulterated sesame oil

Fourier transform infrared (FTIR) spectroscopy is established as an effective and fast method for the confirmation of the authenticity of food and among other, edible oils. However, no standard procedure is available for applying preprocessing as a vital step in obtaining accurate results from spectra. This study proposes a methodological approach to preprocessing FTIR spectra of sesame oil adulterated with vegetable oils (canola oil, corn oil, and sunflower oil). The primary preprocessing methods investigated are orthogonal signal correction (OSC), standard normal variate transformation (SNV), and extended multiplicative scatter correction (EMSC). Other preprocessing methods are used both as standalone methods and in combination with the primary preprocessing methods. The preprocessing results are compared using partial least squares regression (PLSR). OSC alone or with detrending were the most accurate in predicting the adulteration level of sesame oil, with a maximum coefficient of prediction (R²_p) range of 0.910 to 0.971 for different adulterants.

Flavour by design: food-grade lactic acid bacteria improve the volatile aroma spectrum of oat milk, sunflower seed milk, pea milk, and faba milk towards improved flavour and sensory perception

BACKGROUND

The global market of plant-based milk alternatives is continually growing. Flavour and taste have a key impact on consumers' selection of plant-based beverages. Unfortunately, natural plant milks have only limited acceptance. Their typically bean-like and grassy notes are perceived as "off-flavours" by consumers, while preferred fruity, buttery, and cheesy notes are missing. In this regard, fermentation of plant milk by lactic acid bacteria (LAB) appears to be an appealing option to improve aroma and taste.

RESULTS

In this work, we systematically studied LAB fermentation of plant milk. For this purpose, we evaluated 15 food-approved LAB strains to ferment 4 different plant milks: oat milk (representing cereal-based milk), sunflower seed milk (representing seed-based milk), and pea and faba milk (representing legume-based milk). Using GC‒MS analysis, flavour changes during anaerobic fermentations were studied in detail. These revealed species-related and plant milk-related differences and highlighted several well-performing strains delivered a range of beneficial flavour changes. A developed data model estimated the impact of individual flavour compounds using sensory scores and predicted the overall flavour note of fermented and nonfermented samples. Selected sensory perception tests validated the model and allowed us to bridge compositional changes in the flavour profile with consumer response.

CONCLUSION

Specific strain-milk combinations provided quite different flavour notes. This opens further developments towards plant-based products with improved flavour, including cheesy and buttery notes, as well as other innovative products in the future. S. thermophilus emerged as a well-performing strain that delivered preferred buttery notes in all tested plant milks. The GC‒MS-based data model was found to be helpful in predicting sensory perception, and its further refinement and application promise enhanced potential to upgrade fermentation approaches to flavour-by-design strategies.

Rapid analysis of meat floss origin using a supervised machine learning-based electronic nose towards food authentication

Authentication of meat floss origin has been highly critical for its consumers due to existing potential risks of having allergic diseases or religion perspective related to pork-containing foods. Herein, we developed and assessed a compact portable electronic nose (e-nose) comprising gas sensor array and supervised machine learning with a window time slicing method to sniff and to classify different meat floss products. We evaluated four different supervised learning methods for data classification (i.e., linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbors (k-NN), and random forest (RF)). Among them, an LDA model equipped with five-window-extracted feature yielded the highest accuracy values of >99% for both validation and testing data in discriminating beef, chicken, and pork flosses. The obtained e-nose results were correlated and confirmed with the spectral data from Fourier-transform infrared (FTIR) spectroscopy and gas chromatography-mass spectrometry (GC-MS) measurements. We found that beef and chicken had similar compound groups (i.e., hydrocarbons and alcohol). Meanwhile, aldehyde compounds (e.g., dodecanal and 9-octadecanal) were found to be dominant in pork products. Based on its performance evaluation, the developed e-nose system shows promising results in food authenticity testing, which paves the way for ubiquitously detecting deception and food fraud attempts.

Predicting the storage time of green tea by myricetin based on surface-enhanced Raman spectroscopy

The quality of green tea changes rapidly due to the oxidation and degradation of polyphenols during storage. Herein, a simple and fast Surface-enhanced Raman spectroscopy (SERS) strategy was established to predict changes in green tea during storage. Raman spectra of green tea with different storage times (2020-2015) were acquired by SERS with silver nanoparticles. The PCA-SVM model was established based on SERS to quickly predict the storage time of green tea, and the accuracy of the prediction set was 97.22%. The Raman peak at 730 cm^-1 caused by myricetin was identified as a characteristic peak, which increased with prolonged storage time and exhibited a linear positive correlation with myricetin concentration. Therefore, SERS provides a convenient method for identifying the concentration of myricetin in green tea, and myricetin can function as an indicator to predict the storage time of green tea.

Identification of agricultural quarantine materials in passenger's luggage using ion mobility spectroscopy combined with a convolutional neural network

As economic globalization intensifies, the recent increase in agricultural products and travelers from abroad has led to an increase in the probability of invasive alien species. A major pathway for invasive alien species is agricultural quarantine materials (AQMs) in travelers' baggage. Thus, it is meaningful to develop efficient methods for early detection and prompt action against AQMs. In this study, a method based on the combination of odor detection of AQMs using ion mobility spectroscopy (IMS) and convolutional neural network (CNN) analysis for the identification of AQM species in luggage was developed. Two different ways were investigated to feed the IMS data of AQMs into the CNN, either as one-dimensional data (1D) (as a spectrum) or as two-dimensional data (2D) (as an IMS topographic map). The performances of CNN models were also compared to those of the commonly used classification algorithms: partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA). By doing gradient-weighted class activation mapping (Grad-CAM), the essential IMS feature regions from the CNN models to predict different AQM species were also identified. The results of this research demonstrated that the application of the CNN to the IMS data of AQMs yielded superior classification performance compared to PLS-DA and SIMCA. Especially, the CNN-2D model which utilized the IMS topographic map as input achieved the best classification accuracy both on the calibration and validation sets. In addition, the Grad-CAM method had an ability to detect critical discriminating spectral regions for different types of AQM samples, and could provide explanation for the CNNs' decision-making. Despite the inherent limitations of the present analytical protocol, the results showed that the method of IMS in combination with a CNN has great potential to be a complement for sniffer dogs and X-ray imaging techniques to detect AQMs.

Changes of volatile substance composition during processing of nine-processed tangerine peel (Jiuzhi Chenpi) determined by gas chromatography-ion mobility spectrometry

Nine-processed tangerine peel (Jiuzhi Chenpi in Chinese) is a famous Chinese traditional snack. The composition and contents of volatile substances during its processing is unclear. Gas chromatography combined with ion mobility spectrometry (GC-IMS) was applied to determine the characteristic changes of volatile components throughout the production process. Four stages such as untreated dry tangerine peel (raw material), debittered tangerine peel, pickled tangerine peel, and final product were examined. A total of 110 flavor compounds including terpenes, alcohols, aldehydes, ketones, esters, acids, and two others were successfully detected in tangerine peel samples across the various production stages. There were abundant amounts of terpenes contributing to the flavor, including limonene, gamma-terpinene, alpha-pinene, myrcene, beta-pinene, and alpha-thujene which were reduced at the later stage of production. Large amounts of esters and alcohols such as methyl acetate, furfuryl acetate, ethyl acetate, benzyl propionate, 2-hexanol, linalool, and isopulegol, were diminished at the early stage of processing, i.e., soaking for debittering. One the other hand, the final product contained increased amount of aldehydes and ketones including pentanal, hexanal, 2-hexenal, 2-heptenal (E), 2-pentenal (E), 1-penten-3-one, 6-methyl-5-hepten-2-one, 2-methyl-2-propenal, and 2-cyclohexen-1-one, and very high level of acetic acid. Present findings help to understand the formation of the unique flavor of nine-processed tangerine peel and provide a scientific basis for the optimization of processing methods and quality control.

Impact of the Covering Vegetable Oil on the Sensory Profile of Canned Tuna of Katsuwonus pelamis Species and Tuna’s Taste Evaluation Using an Electronic Tongue

The impact of the covering vegetable oil (sunflower oil, refined olive oil and extra virgin olive oil, EVOO) on the physicochemical and sensory profiles of canned tuna (Katsuwonus pelamis species) was evaluated, using analytical techniques and a sensory panel. The results showed that canned tuna covered with EVOO possesses a higher content of total phenols and an enhanced antioxidant capacity. This covering medium also increased the appreciated redness-yellowness color of the canned tuna, which showed a higher chromatic and intense color. Olfactory and kinesthetic sensations were significantly dependent on the type of oil used as covering medium. Tuna succulence and adhesiveness were promoted by the use of EVOO, which also contributed to decreasing the tuna-related aroma sensations. The tuna sensory data could be successfully used to identify the type of vegetable oil used. Moreover, a potentiometric electronic tongue allowed discriminating between the canned tuna samples according to the vegetable oil used (mean sensitivity of 96 ± 8%; repeated K-fold cross-validation) and the fruity intensity of the EVOO (mean sensitivity of 100%; repeated K-fold cross-validation). Thus, the taste sensor device could be a practical tool to verify the authenticity of the declared covering medium in canned tuna and to perceive the differences in consumers’ taste.

Effects of Fruit Shading on Gene and Protein Expression During Starch and Oil Accumulation in Developing Styrax tonkinensis Kernels

Styrax tonkinensis has great potential as a biofuel feedstock source having industrial oilseeds with excellent fatty acids (FAs) composition and good fuel properties. Photosynthesis in the developing pericarp could affect the carbon distribution in kernel. During kernel development, more carbon sources are allocated to starch rather than lipid, when the pericarp photosynthesis is reduced by fruit shading treatment. After shading the fruits at 50 days after flowering (DAF), samples of shaded fruit (FSK) and controls (CK) were collected at 80 DAF and analyzed using the proteomic method. We identified 3,181 proteins, of which 277 were differentially expressed proteins, all downregulated in the FSK group. There were 56 proteins found involved in carbohydrate metabolism and lipid biosynthesis leading to oil accumulation with their iTRAQ ratios of FSK/CK ranging from 0.7123 to 1.1075. According to the qRT-PCR analyses, the key genes related to FA and triacylglycerol (TAG) biosynthesis were significantly downregulated between 60 and 90 DAF especially at 80 DAF, while the key genes involved in starch biosynthesis and FA desaturase had no significant difference between the two groups at 80 DAF. Fruit shading is a negative treatment for lipid accumulation but not starch accumulation by restraining enzymic protein expression involved in FA and TAG biosynthesis during S. tonkinensis kernel development.

Validation Study on the Simultaneous Quantitation of Multiple Wine Aroma Compounds with Static Headspace-Gas Chromatography-Ion Mobility Spectrometry

A new quantitative method based on static headspace-gas chromatography-ion mobility spectrometry (SHS-GC-IMS) is proposed, which enables the simultaneous quantitation of multiple aroma compounds in wine. The method was first evaluated for its stability and the necessity of using internal standards as a quality control measure. The two major hurdles in applying GC-IMS in quantitation studies, namely, nonlinearity and multiple ion species, were also investigated using the Boltzmann function and generalized additive model (GAM) as potential solutions. Metrics characterizing the model performance, including root mean squared error, bias, limit of detection, limit of quantitation, repeatability, reproducibility, and recovery, were investigated. Both nonlinear fitting methods, Boltzmann function and GAM, were able to return desirable analytical outcomes with an acceptable range of error. Potential pitfalls that would cause inaccurate quantitation, that is, effects of ethanol content and competitive ionization, were also discussed. The performance of the SHS-GC-IMS method was subsequently compared against that of a currently established method, namely, GC-MS, using commercial wine samples. These findings provide an initial validation of a GC-IMS-based quantitation method, as well as a starting point for further enhancing the analytical scope of GC-IMS.

Volatile-Olfactory Profiles of cv. Arbequina Olive Oils Extracted without/with Olive Leaves Addition and Their Discrimination Using an Electronic Nose

Oils from cv. Arbequina were industrially extracted together with olive leaves of cv. Arbequina or Santulhana (1%, w/w), and their olfactory and volatile profiles were compared to those extracted without leaves addition (control). The leaves incorporation resulted in green fruity oils with fresh herbs and cabbage olfactory notes, while control oils showed a ripe fruity sensation with banana, apple, and dry hay grass notes. In all oils, total volatile contents varied from 57.5 to 65.5 mg/kg (internal standard equivalents), being aldehydes followed by esters, hydrocarbons, and alcohols the most abundant classes. No differences in the number of volatiles were observed. The incorporation of cv. Arbequina or Santulhana leaves significantly reduced the total content of alcohols and esters (minus 37–56% and 10–13%, respectively). Contrary, cv. Arbequina leaves did not influence the total content of aldehydes or hydrocarbons, while cv. Santulhana leaves promoted a significant increase (plus 49 and 10%, respectively). Thus, a leaf-cultivar dependency was observed, tentatively attributed to enzymatic differences related to the lipoxygenase pathway. Olfactory or volatile profiles allowed the successful unsupervised differentiation of the three types of studied cv. Arbequina oils. Finally, a lab-made electronic nose was applied to allow the nondestructive discrimination of cv. Arbequina oils extracted with or without the incorporation of olive leaves (100% and 99 ± 5% of correct classifications for leave-one-out and repeated K-fold cross-validation variants), being a practical tool for ensuring the label correctness if future commercialization is envisaged. Moreover, this finding also strengthened that olive oils extracted with or without olive leaves incorporation possessed quite different olfactory patterns, which also depended on the cultivar of the olive leaves.

Non-Target Detection of Diversity of Volatile Chlorine Compounds in Frying Oil and Study on the Influencing Factors of Their Formation

Headspace-gas-chromatography ion-mobility spectrometry (HS-GC-IMS) proved the diversity of volatile chlorinated compounds (VCCs) in frying oil in this work. First, the VCCs were obtained by headspace by heating the frying oil at 80 °C for 30 min. Then, those compounds were separated by GC capillary column in the first dimension and by IMS in the second dimension, respectively. And at last, those compounds were detected in negative ion mode for non-targeting. The study results indicated that VCCs' formation depends on the contents of NaCl and water, heating temperature and time, and the types of oil. The refining process does not affect the detection of VCCs, indicating the durability of such targets as indicators for assessing deep-frying oil. Using HS-GC-IMS, the VCCs were detected to evaluate 16 authentic refined deep-frying oils from the market with an accuracy of 100%.

Untargeted Headspace-Gas Chromatography-Ion Mobility Spectrometry in Combination with Chemometrics for Detecting the Age of Chinese Liquor (Baijiu)

This paper proposes the combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and chemometrics as a method to detect the age of Chinese liquor (Baijiu). Headspace conditions were optimized through single-factor optimization experiments. The optimal sample preparation involved diluting Baijiu with saturated brine to 15% alcohol by volume. The sample was equilibrated at 70 °C for 30 min, and then analyzed with 200 μL of headspace gas. A total of 39 Baijiu samples from different vintages (1998–2019) were collected directly from pottery jars and analyzed using HS-GC-IMS. Partial least squares regression (PLSR) analysis was used to establish two discriminant models based on the 212 signal peaks and the 93 identified compounds. Although both models were valid, the model based on the 93 identified compounds discriminated the ages of the samples more accurately according to the goodness of fit value (R²) and the root mean square error of prediction (RMSEP), which were 0.9986 and 0.244, respectively. Nineteen compounds with variable importance for prediction (VIP) scores > 1, including 11 esters, 4 alcohols, and 4 aldehydes, played vital roles in the model established by the 93 identified compounds. Overall, we determined that HS-GC-IMS combined with PLSR could serve as a rapid and accurate method for detecting the age of Baijiu.

Effects of ethanol, activated carbon, and activated kaolin on perilla seed oil: Volatile organic compounds, physicochemical characteristics, and fatty acid composition

Perilla seed oil (PSO) has a special aromatic odor, which is unpleasant to the personal preferences of some consumers. To this end, this article evaluated the differences in volatile organic compounds (VOCs), physicochemical characteristics, and fatty acid composition of PSO treated with ethanol (PSO-EA), activated carbon (PSO-AC), and activated kaolin (PSO-AK). The results showed that in the PSO, PSO-EA, PSO-AC, and PSO-AK samples, the content of linolenic acid, oleic acid, and linoleic acid hardly changed. Among the physicochemical characteristics of the four samples, the color difference between PSO and PSO-EA was greater than the color difference between PSO and PSO-AC, PSO-AK. The three treatment methods had the greatest impact on the PSO peroxide value but had little effect on other indicators. Gas chromatography-ion mobility spectrum results identified 28 known volatiles, of which aldehydes, alkenals, alcohols, ketones, and esters were the main groups. Fingerprint analysis found that PSO had an aromatic odor, which includes 1-hexanol, hexanal, and 2-pentylfuran; the removal effect of ethanol on VOCs in PSO was better than that of activated carbon and activated kaolin. The difference between the four oil samples was found from the strength of the VOCs' signals in a two-dimensional map. From the principal components analysis and the "nearest neighbor" fingerprint analysis, it was found that PSO is generally quite different from PSO-EA, PSO-AC, and PSO-AK, while in the "nearest neighbor" fingerprint analysis, PSO-AC and PSO-AK are similar in general. In short, PSO will have better applications in the food field. PRACTICAL APPLICATION: Treatment of PSO with ethanol, activated carbon, and activated kaolin is conducive to the comprehensive utilization of edible resources. In this work, ethanol, activated carbon, and activated kaolin were used to remove VOCs in PSO, and PSO-EA, PSO-AC, and PSO-AK were obtained. The perilla seed oil after these three treatment methods was tested for VOCs, physicochemical characteristics, and fatty acid composition. They can meet the needs of more consumers without affecting the fatty acid composition in the PSO, and have broad development prospects.

Full Workflows for the Analysis of Gas Chromatography-Ion Mobility Spectrometry in Foodomics: Application to the Analysis of Iberian Ham Aroma

Gas chromatography—ion mobility spectrometry (GC-IMS) allows the fast, reliable, and inexpensive chemical composition analysis of volatile mixtures. This sensing technology has been successfully employed in food science to determine food origin, freshness and preventing alimentary fraud. However, GC-IMS data is highly dimensional, complex, and suffers from strong non-linearities, baseline problems, misalignments, peak overlaps, long peak tails, etc., all of which must be corrected to properly extract the relevant features from samples. In this work, a pipeline for signal pre-processing, followed by four different approaches for feature extraction in GC-IMS data, is presented. More precisely, these approaches consist of extracting data features from: (1) the total area of the reactant ion peak chromatogram (RIC); (2) the full RIC response; (3) the unfolded sample matrix; and (4) the ion peak volumes. The resulting pipelines for data processing were applied to a dataset consisting of two different quality class Iberian ham samples, based on their feeding regime. The ability to infer chemical information from samples was tested by comparing the classification results obtained from partial least-squares discriminant analysis (PLS-DA) and the samples’ variable importance for projection (VIP) scores. The choice of a feature extraction strategy is a trade-off between the amount of chemical information that is preserved, and the computational effort required to generate the data models.

E-Nose and Olfactory Assessment: Teamwork or a Challenge to the Last Data? The Case of Virgin Olive Oil Stability and Shelf Life

Electronic nose (E-nose) devices represent one of the most trailblazing innovations in current technological research, since mimicking the functioning of the biological sense of smell has always represented a fascinating challenge for technological development applied to life sciences and beyond. Sensor array tools are right now used in a plethora of applications, including, but not limited to, (bio-)medical, environmental, and food industry related. In particular, the food industry has seen a significant rise in the application of technological tools for determining the quality of edibles, progressively replacing human panelists, therefore changing the whole quality control chain in the field. To this end, the present review, conducted on PubMed, Science Direct and Web of Science, screening papers published between January 2010 and May 2021, sought to investigate the current trends in the usage of human panels and sensorized tools (E-nose and similar) in the food industry, comparing the performances between the two different approaches. In particular, the focus was mainly addressed towards the stability and shelf life assessment of olive oil, the main constituent of the renowned “Mediterranean diet”, and nowadays appreciated in cuisines from all around the world. The obtained results demonstrate that, despite the satisfying performances of both approaches, the best strategy merges the potentialities of human sensory panels and technological sensor arrays, (i.e., E-nose somewhat supported by E-tongue and/or E-eye). The current investigation can be used as a reference for future guidance towards the choice between human panelists and sensorized tools, to the benefit of food manufacturers.

Authentication of the Montanera Period on Carcasses of Iberian Pigs by Using Analytical Techniques and Chemometric Analyses

The potential of two complementary analytical techniques (near infrared spectroscopy, NIRS and gas chromatography-ion mobility spectrometry, GC-IMS) was used to establish the time that Iberian pigs have been fed on acorns and pasture and to verify their genetic purity. For both techniques it was neither necessary to carry out any chemical treatment in advance nor to identify individual compounds. The results showed that both the NIR spectrum and the spectral fingerprint obtained by GC-IMS were affected by the time that the Iberian pig feeds on natural resources. High percentages of correct classification were achieved in the calibration for both techniques: >98% for the days of montanera and >96% for the breed by NIRS and >99% for the days of montanera and >98% for the breed by GC-IMS. The results obtained showed that NIR spectra taken from intact samples is a quick classification method according to the time of montanera and breed.

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS.

Age Discrimination of Chinese Baijiu Based on Midinfrared Spectroscopy and Chemometrics

Baijiu is a traditional and popular Chinese liquor which is affected by the storage time. The longer the storage time of Baijiu is, the better its quality is. In this paper, the raw and mellow Baijiu samples from different storage time are discriminated accurately throughout midinfrared (MIR) spectroscopy and chemometrics. Firstly, changing regularities of the substances in Chinese Baijiu are discussed by gas chromatography-mass spectrometry (GC-MS) during the aging process. Then, infrared spectrums of Baijiu samples are processed by smoothing, multivariate baseline correction, and the first and second derivative processing, but no significant variation can be observed. Next, the spectral date pretreatment methods are constructively introduced, and principal component analysis (PCA) and discriminant analysis (DA) are developed for data analyses. The results show that the accuracy rates of samples by the DA method in calibration and validation sets are 91.7% and 100%, respectively. Consequently, an identification model based on support vector machine (SVM) and PCA is established combined with the grid search strategy and cross-validation methods to discriminate the age of Chinese Baijiu validly, where 100% classification accuracy rate is obtained in both training and test sets.

Characteristic volatile organic compounds in “HeTao” melon and other cultivars grown in Hetao region analyzed by HS-GC-IMS

The headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) was used to compare the volatile organic components of the Hetao melon and six other cultivars of melon grown in the Hetao region of China. The results showed that the common VOCs that could be qualitatively identified from the 7 different melon samples were 35 monomers and dimers of certain compounds, mainly including alcohols, esters, aldehydes, terpenes, acids and pyridines. Hexyl acetate, 3-methylbutyl acetate, ethyl acetate and ethyl formate were predominant VOCs in seven melon cultivars. Among them, Xizhoumi No. 25 (XZM25) had 3 unique volatile organic components: 3-methylbutanal, benzaldehyde and nonanal. Xizhoumi No. 17 (XZM17) had 3 unique volatile organic components: alpha-pinene, linalool and (E)-2-hexenol. Jinhongmi (JHM) had 1 unique volatile organic component: ethyl pentanoate. The Hetao melon (HLS) contained 3 unique volatile organic components: heptanal, 2-ethyl-6-methyl pyrazine and 3-methyl valeric acid. Yinmi (YM) had 2 unique volatile organic components: 3-methylbutanol and 1-butanol, and Huangjinmi (HJM) had 1 unique volatile organic component: limonene. YM, GMB2010, HLS and JHM were similar based on the principal component analysis. This research analyzed the flavor components of different melon cultivars grown in the Hetao region of China for the first time.

Volatile Profile Characterization of Winter Jujube from Different Regions via HS-SPME-GC/MS and GC-IMS

A combined untargeted and targeted approach was established for fingerprinting volatile organic compounds in winter jujubes from eight regions of China. Volatiles, including alcohols, aldehydes, acids, esters, and alkenes, were identified by gas chromatography-ion mobility spectrometry (GC-IMS). Benzyl alcohol, octanoic acid, 2-hexenal, linalool, 2-nonenal, and ethyl decanoate were the most common compounds present in all jujubes. Principal component analysis (PCA) from GC-IMS and untargeted E-nose showed that the main volatile organic compounds (VOCs) of most jujubes were similar. The volatile organic compounds of winter jujubes from Yuncheng city, Shanxi province, and Aksu region, Xinjiang province, were significantly different from those from other regions. 1-Penten-3-ol, ethyl hexanoate, methyl laurate, and 2-formyltoluene were the markers of XJAKS with green and fruity aroma, and SXYC could be labeled by acetone and 2-methoxyphenol with woody and pungent aroma. GC-IMS was an effective method for volatile fingerprinting of jujubes with high sensitivity and accuracy.

Behavioral responses of Platycladus orientalis plant volatiles to Phloeosinus aubei by GC-MS and HS-GC-IMS for discrimination of different invasive severity

In recent years, the invasive cypress bark beetle (Phloeosinus aubei) has caused extensive damage to Platycladus orientalis plants in China, but its infestation is hard to monitor in the early stages. In this study, gas chromatography-mass spectrometry (GC-MS) was initially employed to investigate the volatile organic compound (VOC) emissions of P. aubei-infested P. orientalis saplings. The emissions of total sesquiterpenes were dominating (84-86% of total VOCs) and increased by 3.09-fold in P. aubei-damaged P. orientalis samples compared to undamaged samples, and the monoterpenes, aromatic compounds, and ketone emissions also had varying degrees of increase between 1.39-fold and 5.65-fold. Based on this variation, gas chromatography-ion mobility spectrometry (GC-IMS) was applied, as an untargeted analytical approach, to discriminate P. orientalis samples with different invasive severity. Two different features derived from GC-IMS data were adopted as the input information for classification and prediction models. Results showed that grid search support vector machine (GS-SVM) combined with multilinear principal component analysis (MPCA) based on spectral fingerprint achieved the best classification performances (> 88.98%), and partial least squares discriminant analysis (PLSR) method can accurately predict the pest numbers (R² > 0.9423 and RMSE < 0.9827). In a word, the VOC profiling-based approach had the potential for evaluating P. aubei invasive severity and pest management.

Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

Rapid varieties classification of crop seeds is significant for breeders to screen out seeds with specific traits and market regulators to detect seed purity. However, collecting high-quality, large-scale samples takes high costs in some cases, making it difficult to build an accurate classification model. This study aimed to explore a rapid and accurate method for varieties classification of different crop seeds under the sample-limited condition based on hyperspectral imaging (HSI) and deep transfer learning. Three deep neural networks with typical structures were designed based on a sample-rich Pea dataset. Obtained the highest accuracy of 99.57%, VGG-MODEL was transferred to classify four target datasets (rice, oat, wheat, and cotton) with limited samples. Accuracies of the deep transferred model achieved 95, 99, 80.8, and 83.86% on the four datasets, respectively. Using training sets with different sizes, the deep transferred model could always obtain higher performance than other traditional methods. The visualization of the deep features and classification results confirmed the portability of the shared features of seed spectra, providing an interpreted method for rapid and accurate varieties classification of crop seeds. The overall results showed great superiority of HSI combined with deep transfer learning for seed detection under sample-limited condition. This study provided a new idea for facilitating a crop germplasm screening process under the scenario of sample scarcity and the detection of other qualities of crop seeds under sample-limited condition based on HSI.

Instrumental Techniques to Classify Olive Oils according to Their Quality

This review includes an update of the publications on quality classification of olive oils into extra, virgin or lampante olive oil categories. Nowadays, the official method to carry out this classification is time-consuming and, sometimes, it is not systematic and/or objective. It is based on conventional physicochemical analysis and on a sensorial tasting of olive oils carried out by a panel of experts. The aim of this review was to explore and give value to the alternative techniques reported in the bibliography to complement the current official methods established for that classification of olive oils. Specifically considered were non-separation and separation analytical techniques which could contribute to correctly classify olive oils according to their physicochemical and/or sensorial characteristics. An in-depth description has been written on the methods used to differentiate these three types of olive oils and the main advantages and disadvantages of the proposed procedures. The techniques here reviewed could be a real and fast option to complement or even substitute some of the analysis included in the official method. Finally, general trends and detected difficulties found to address this issue have been discussed throughout the article.

Application of gas chromatography-ion mobility spectrometry (GC-IMS) and ultrafast gas chromatography electronic-nose (uf-GC E-nose) to distinguish four Chinese freshwater fishes at both raw and cooked status

The volatile organic compounds (VOCs) in four Chinese freshwater fishes (i.e., Hypophthalmichthys molitrix (H), Aristichthys nobilis (A), Lateolabrax japonicus (L), Parabramis pekinensis (P)) were separated using gas chromatography-ion mobility spectrometry (GC-IMS) and ultrafast gas chromatography electronic-nose (uf-GC E-nose). Principal component analysis (PCA) was applied to distinguish the VOCs identified from the four freshwater fishes in both raw and cooked states. Twenty compounds were identified from the spectral database of GC-IMS, including five aldehydes, eight alcohols, six ketones, and three esters. In addition, using GC E-nose, 32 compounds were isolated by the first column MTX-5, and 24 compounds were isolated by the second column MXT-1701. PCA results showed that the four fishes could be well discriminated against. The odor profiles of raw and cooked fishes were clearly different. This study demonstrated that specific signals provided from GC-IMS could differentiate freshwater fishes. GC-IMS and uf-GC E-nose could be developed further to distinguish aquatic products based on VOCs. PRACTICAL APPLICATIONS: Two new methods, gas chromatography-ion mobility spectrometry (GC-IMS) and ultrafast gas chromatography electronic-nose (uf-GC E-nose), were used to analyze the volatile organic compounds (VOCs) in four Chinese freshwater fishes at raw and cooked status. GC-IMS has the characteristics of fast detection speed and high sensitivity. The accuracy of the qualitative analysis of the compounds is better with GC-IMS (larger data volume, leading to a better in-depth statistical analysis). Uf-GC E-nose could provide a nondestructive, fast, relatively low cost, and trustworthy way for flavor analysis. According to the techniques, the established fingerprints of VOCs provided an additional tool for food analysis.