Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning.

Schizophrenia is a severe mental disorder that impairs a person's mental, social, and emotional faculties gradually. Detection in the early stages with an accurate diagnosis is crucial to remedying the patients. This study proposed a new method to classify schizophrenia disease in the rest state based on neurologic signals achieved from the brain by electroencephalography (EEG). The datasets used consisted of 28 subjects, 14 for each group, which are schizophrenia and healthy control. The data was collected from the scalps with 19 EEG channels using a 250 Hz frequency. Due to the brain signal variation, we have decomposed the EEG signals into five sub-bands using a band-pass filter, ensuring the best signal clarity and eliminating artifacts. This work was performed with several scenarios: First, traditional techniques were applied. Secondly, augmented data (additive white Gaussian noise and stretched signals) were utilized. Additionally, we assessed Minimum Redundancy Maximum Relevance (MRMR) as the features reduction method. All these data scenarios are applied with three different window sizes (epochs): 1, 2, and 5 s, utilizing six algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy entropy (LogEn), Shannon Entropy (ShnEn), and kurtosis. The L2-normalization method was applied to the derived features, positively affecting the results. In terms of classification, we applied four algorithms: K-nearest neighbor (KNN), support vector machine (SVM), quadratic discriminant analysis (QDA), and ensemble classifier (EC). From all the scenarios, our evaluation showed that SVM had remarkable results in all evaluation metrics with LogEn features utilizing a 1-s window size, impacting the diagnosis of Schizophrenia disease. This indicates that an accurate diagnosis of schizophrenia can be achieved through the right features and classification model selection. Finally, we contrasted our results to recently published works using the same and a different dataset, where our method showed a notable improvement.

Flavoromic analysis of wines using gas chromatography, mass spectrometry and sensory techniques.

Various sensory perceptions drive the quality and typicality of wines, with the volatile profile playing a fundamental role in the characteristics of odor, aroma and consequently flavor, which combines the smell (odor and aroma), taste, and trigeminal sensations. Efforts have been made in both the field of instrumental and sensory analysis to understand the relationship of volatile compounds with sensory attributes in omics approaches. Gas chromatography (monodimensional and two-dimensional (heartcutting and comprehensive)) associated with mass spectrometry (GC/MS, GC-GC/MS and GCxGC/MS) and chemometric tools have contributed to foodomics analyses, specifically those linked to metabolomics/volatilomics. These tools, along with the elucidation of sensory properties (sensomics), lead to advanced results in the field of flavoromics. They also help to define the best practices in both vineyard management and winemaking that enable the production of high-quality wines. The objective of this review is to report the challenges of determining the volatile profile of wines, pointing out the ways that can be followed in successful identification and quantification of volatile compounds. The state of the art of sensory evaluation methods is also addressed, providing information that helps in choosing the most appropriate sensory method to be conducted with chromatographic analysis to achieve more in-depth results in the field of flavoromics.

Quantitative descriptive analysis as a strategic tool in research activities relating to innovative meat tenderisation technologies.

Sensory analysis plays a significant role in developing innovative technology from prototype to industrial stage, and above all, in the meat industry. The starting hypothesis is that the quantitative descriptive analysis is crucial to optimise an innovative process for tenderising meat before the scale-up stage because it provides information that instrumental and consumer science analyses cannot achieve. With this in mind, the present study describes the detailed protocol of the quantitative descriptive analysis, which was developed and optimised to contribute to the prototype development stage of new meat tenderising technology. This study applied the quantitative descriptive analysis to evaluate the sensory characteristics of semitendinosus beef meats submitted to the tenderising process by combining exogenous enzymes and ultra-sound radiation treatments. A correlation analysis was performed among sensory and instrumental data. A significant and negative correlation was found only among texture parameters evaluated by sensory and instrumental parameters (R > -0.81 and P < 0.05). Conversely, no significant correlation (P > 0.05) was found between sensory and instrumental chromatic characteristics. Moreover, the quantitative descriptive analysis was a valuable tool because it provided precious information on the appearance of the treated raw meat (score less than 6), which was not detected by instrumental analyses. This information is precious because the appearance of raw meat is fundamental to the consumer buying decision process. Based on the results obtained through sensory analysis, we could highlight the necessity of optimising technological processing before the industrialisation stage to avoid a probable failure of this production method when applied to the market.

Molecular mechanism for the influence of yam starch multiscale structure on the sensory texture of cooked yam.

Yam is a dual-purpose crop as both medicine and food. However, the mechanism controlling the eating quality of yam remains to be elucidated. This study explored the influence of starch multiscale structure on the texture of yam. The results indicated that FS and RC yam have higher hardness and chewiness, while BZ, XM, and PL yam possess waxiness, Fineness, and Stickiness. Statistically, high amylose (AM) can increase hardness, chewiness, and compactness; and average molecular size (Rh) is positively correlated with stickiness, fineness, and waxiness. Specifically, medium- and long-chain amylose (1000 < X ≤ 10,000) and amylopectin (24 < X ≤ 100), particularly medium-chain amylose (1000 < X ≤ 5000) and long-chain amylopectin (24 < X ≤ 36), primarily affect sensory and rheological stickiness. The long chains of amylose form a straight chain interspersed in the crystalline and amorphous regions to support the entire lamellar structure. Higher proportion of amylose long chains, promoting the starch's structural rigidity, which in turn enhanced its hardness-related attributes. Moreover, a higher ratio of long chains within amylopectin results in tightly intertwined adjacent outer chains, forming double helix crystalline zones. This consequently augmenting the texture quality linked to stickiness-related attributes.

Targeted CQA analytical control strategy for commercial antibody products: Replacing ion-exchange chromatography methods for charge heterogeneity with multi-attribute monitoring.

Peptide mapping with mass spectrometry (MS) is an important tool for protein characterization in the biopharmaceutical industry. Historically, peptide mapping monitors post-translational modifications (PTMs) of protein products and process intermediates during development. Multi-attribute monitoring (MAM) methods have been used previously in commercial release and stability testing panels to ensure control of selected critical quality attributes (CQAs). Our goal is to use MAM methods as part of an overall analytical testing strategy specifically focused on CQAs, while removing or replacing historical separation methods that do not effectively distinguish CQAs from non-CQAs due to co-elution. For example, in this study, we developed a strategy to replace a profile-based ion-exchange chromatography (IEC) method using a MAM method in combination with traditional purity methods to ensure control of charge variant CQAs for a commercial antibody (mAb) drug product (DP). To support this change in commercial testing strategy, the charge variant CQAs were identified and characterized during development by high-resolution LC-MS and LC-MS/MS. The charge variant CQAs included PTMs, high molecular weight species, and low molecular weight species. Thus, removal of the IEC method from the DP specification was achieved using a validated LC-MS MAM method on a QDa system to directly measure the charge variant PTM CQAs in combination with size exclusion chromatography (SE-HPLC) and capillary electrophoresis (CE-SDS) to measure the non-PTM charge variant CQAs. Bridging data between the MAM, IEC, and SE-HPLC methods were included in the commercial marketing application to justify removing IEC from the DP specification. We have also used this MAM method as a test for identity to reduce the number of QC assays. This strategy has received approvals from several health authorities.

Construction of novel multi-epitope-based diagnostic biomarker HP16118P and its application in the differential diagnosis of Mycobacterium tuberculosis latent infection.

Tuberculosis (TB) is an infectious disease that significantly threatens human health. However, the differential diagnosis of latent tuberculosis infection (LTBI) and active tuberculosis (ATB) remains a challenge for clinicians in early detection and preventive intervention. In this study, we developed a novel biomarker named HP16118P, utilizing 16 helper T lymphocyte (HTL) epitopes, 11 cytotoxic T lymphocyte (CTL) epitopes, and 8 B cell epitopes identified from 15 antigens associated with LTBI-RD using the IEDB database. We analyzed the physicochemical properties, spatial structure, and immunological characteristics of HP16118P using various tools, which indicated that it is a hydrophilic and relatively stable alkaline protein. Furthermore, HP16118P exhibited good antigenicity and immunogenicity, while being non-toxic and non-allergenic, with the potential to induce immune responses. We observed that HP16118P can stimulate the production of high levels of IFN-γ+ T lymphocytes in individuals with ATB, LTBI, and health controls. IL-5 induced by HP16118P demonstrated potential in distinguishing LTBI individuals and ATB patients (p=0.0372, AUC=0.8214, 95% CI [0.5843 to 1.000]) with a sensitivity of 100% and specificity of 71.43%. Furthermore, we incorporated the GM-CSF, IL-23, IL-5, and MCP-3 induced by HP16118P into 15 machine learning algorithms to construct a model. It was found that the Quadratic discriminant analysis model exhibited the best diagnostic performance for discriminating between LTBI and ATB, with a sensitivity of 1.00, specificity of 0.86, and accuracy of 0.93. In summary, HP16118P has demonstrated strong antigenicity and immunogenicity, with the induction of GM-CSF, IL-23, IL-5, and MCP-3, suggesting their potential for the differential diagnosis of LTBI and ATB.

Sensory attributes of fine flavor cocoa beans and chocolate: A systematic literature review.

Fine flavor cocoa (FFC) is known for its unique flavor and aroma characteristics, which vary by region. However, a comprehensive overview of the common sensory attributes used to describe FFC beans and chocolate is lacking. Therefore, a systematic review was conducted to analyze existing literature and identify the most commonly used sensory attributes to describe FFC beans and chocolate. A systematic search of the Web of Science and Scopus databases was conducted in May 2023, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed to ensure transparency and reproducibility. This review summarizes the origins of cocoa and explores their unique flavor profiles, encompassing caramel, fruity, floral, malty, nutty, and spicy notes. Although some origins may exhibit similar unique flavors, they are often described using more specific terms. Another main finding is that although differences in sensory attributes are anticipated at each production stage, discrepancies also arise between liquor and chocolate. Interestingly, fine chocolate as the final product does not consistently retain the distinctive flavors found in the liquor. These findings emphasize the need for precise descriptors in sensory evaluation to capture flavor profiles of each origin. As such, the exploration of attributes from bean to bar holds the potential to empower FFC farmers and chocolate producers to effectively maintain quality control.

All-in-one stability indicating polysorbate 20 degradation root-cause analytics via UPLC-QDa.

Polysorbates (PS) are the most frequently used surfactants to stabilize biologicals. Ironically, these excellent stabilizing non-ionic surfactants have inherent structural properties, which lead to instabilities of their own. Such PS degradation can be triggered by multiple root-causes, like chemical and enzymatic hydrolysis or oxidative degradation. This can on the one hand reduce the concentration of surface-active PS and on the other hand lead to the formation of unfavorable degradants, like poorly soluble free fatty acids (FFA), which may phase separate and form visible FFA particles. Due to the potential criticality of PS degradation in biopharmaceutical formulations, various analytics have been established in recent years not only to monitor the PS content but also to evaluate specific PS markers and crucial degradants. However, in most cases sample preparations and several analytical assays have to be conducted to obtain a comprehensive picture of potential PS degradation root-causes. Here we show a novel approach for PS degradation UPLC-QDa based root-cause analytics, which utilizes previously established analytics for (i) most relevant polysorbate 20 (PS20) esters, (ii) PS20 free fatty acids and (iii) a newly developed method for the evaluation of PS20 specific oxidation markers. Thereby, this triad of analytical methods uses the same sample preparation and detector, which reduces the overall necessary effort, time investment and sample volume. Furthermore, the innovative PS20 oxidation marker method allows to quantify specific concentrations of the determined markers by external calibration and possible perception of oxidative degradation processes prior to relevant losses of PS20 esters, which could serve as an early indication during formulation development. The applicability of this method set was verified using several PS20 containing stress samples, which cover the most relevant root-causes, including acidic and alkaline hydrolysis, enzyme mediated hydrolysis, oxidative AAPH stress and Fe2+/H2O2 mediated degradation as well as autoxidation via long-term storage at elevated temperatures. Overall, this analytical setup has shown to deliver in-depth data about PS20 degradation, which can be used to narrow down the causative stress without the necessity of fundamentally different methods. Therefore, it can be seen as all-in-one solution during sometimes troublesome development of biopharmaceutical formulations, that supports the elucidation of the PS degradation mechanism(s) and thus establish mitigation strategies.

Impact of Leavening Agents on Flavor Profiles and Microbial Communities in Steamed Bread: A Comparative Analysis of Traditional Chinese Sourdough and Commercial Yeast.

Chinese steamed bread (CSB) made with commercial yeasts and traditional Chinese sourdoughs was analyzed for the flavor and microbial communities. Sensory attributes were assessed using quantitative descriptive analysis (QDA). Results showed that commercial yeast CSB-1 (JMMT1), a yeast-based sample, had stronger milky and sweet attributes, while commercial yeast CSB-2 (JMMT2) had more pronounced yeasty attributes. Among the sourdough-based samples, Shandong traditional sourdough steamed bread (SDMT) exhibited a winelike character with a weak sweet aftertaste, whereas Shanxi traditional sourdough steamed bread (SXMT) had a distinct sour attribute and a less prominent floury taste. SAFE-GC-O-MS analysis identified 40 aroma compounds with FD values ≥2, including 33 key aroma compounds with an OAV of ≥1. Compounds such as 2,3-butanediol, decanal, methyl isobutenyl ketone, gamma-nonanolactone, ethyl caprate, 2-ethylhexyl acetate, vanillin, and indole contributed significantly to the diverse aroma profiles. High-throughput sequencing revealed dominant strains: Bacillus in JMMT1, Lactobacillus in JMMT2, Bacillus in SDMT, and Lactobacillus in SXMT. Over two-thirds of the aroma compounds showed correlations with microorganisms. Notably, Acetobacter exhibited a highly significant correlation with butanoic acid, while Lactobacillus played a significant role in the formation of ester flavors. These findings contribute to the flavor evaluation and microbial community analysis of steamed bread made with different leavening agents, providing valuable insights into their relationship.

Direct analysis in real time of high-quality extra virgin olive oils for the rapid and automatic identification of origin trademark.

BACKGROUND: Following our previous research on the differentiation of Italian extra virgin olive oils (EVOOs) by rapid evaporative ionization mass spectrometry coupled to a tandem high resolution mass analyser, the present study deals with the evaluation of another direct mass spectrometry (direct-MS) approach for the rapid and automatic discrimination of EVOOs. In particular, direct analysis in real time (DART-MS) was explored as an ambient MS (AMS) source for the building of a top-quality Italian EVOOs database and fast identification of unknown samples. A single quadrupole detector (QDa) was coupled with DART, taking advantage of a cost-saving, user-friendly and less sophisticated instrumental setup. Particularly, quickstrip cards, located on a moving rail holder, were employed, allowing for the direct analysis of 12 EVOO spots in a total analysis time of 6 min. The aim was to develop a reliable statistical model by applying principal component and linear discriminant analyses to clusterize and classify EVOOs according to geographical origin and cultivar, as main factors determining their nutritional and sensory profiles. RESULTS: Satisfactory results were achieved in terms of identification reliability of unknown EVOOs, as well as false positive risk, thus confirming that the use of AMS combined with chemometrics is a powerful tool against fraudulent activities, without the need for mass accuracy data, which would increase the analysis cost. CONCLUSION: A DART ionization source with a compact and reliable QDa MS analyser allowed for rapid fingerprinting analysis. Furthermore, MS spectra provided quali-quantitative information successfully related to EVOO differentiation. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Exploring the Characteristic Aroma Components of Traditional Fermented Koumiss of Kazakh Ethnicity in Different Regions of Xinjiang by Combining Modern Instrumental Detection Technology with Multivariate Statistical Analysis Methods for Odor Activity Value and Sensory Analysis.

To investigate the characteristic aromatic compounds, present in the traditional fermented koumiss of the Kazakh ethnic group in different regions of Xinjiang, GC-IMS, and GC-MS were used to analyze the volatile compounds in koumiss from four regions. A total of 87 volatile substances were detected, and esters, acids, and alcohols were found to be the main aroma compounds in koumiss. While the types of aroma compounds in koumiss were similar across different regions, the differences in their concentrations were significant and displayed clear regional characteristics. The fingerprint spectrum of GC-IMS, combined with PLS-DA analysis, indicates that eight distinctive volatile compounds, including ethyl butyrate, can be utilized to distinguish between different origins. Additionally, we analyzed the OVA value and sensory quantification of koumiss in different regions. We found that aroma components such as ethyl caprylate and ethyl caprate, which exhibit buttery and milky characteristics, were prominent in the YL and TC regions. In contrast, aroma components such as phenylethanol, which feature a floral fragrance, were more prominent in the ALTe region. The aroma profiles of koumiss from the four regions were defined. These studies provide theoretical guidance for the industrial production of Kazakh koumiss products.

High-throughput identification and determination of antifungal triazoles in human plasma using UPLC-QDa.

Triazoles are common agents for invasive fungal infections, while therapeutic drug monitoring is needed to improve antifungal efficacy and reduce toxicity. This study aimed to exploit a simple and reliable liquid chromatography-mass spectrometry method for high-throughput monitoring of antifungal triazoles in human plasma using UPLC-QDa. Triazoles in plasma were separated by chromatography on a Waters BEH C18 column and detected using positive ions electrospray ionization fitted with single ion recording. M+ for fluconazole (m/z 307.11) and voriconazole (m/z 350.12), M2+ for posaconazole (m/z 351.17), itraconazole (m/z 353.13) and ketoconazole (m/z 266.08, IS) were selected as representative ions in single ion recording mode. The standard curves in plasma showed acceptable linearities over 1.25-40 µg/mL for fluconazole, 0.47-15 µg/mL for posaconazole and 0.39-12.5 µg/mL for voriconazole and itraconazole. The selectivity, specificity, accuracy, precision, recovery, matrix effect, and stability met acceptable practice standards under Food and Drug Administration method validation guidelines. This method was successfully applied to the therapeutic monitoring of triazoles in patients with invasive fungal infections, thereby guiding clinical medication.

Evaluation and Verification of a microRNA Panel Using Quadratic Discriminant Analysis for the Classification of Human Body Fluids in DNA Extracts.

There is significant interest in the use of miRNA analysis for forensic body fluid identification. Demonstrated co-extraction and detection in DNA extracts could make the use of miRNAs a more streamlined molecular body fluid identification method than other RNA-based methods. We previously reported a reverse transcription-quantitative PCR (RT-qPCR) panel of eight miRNAs that classified venous and menstrual blood, feces, urine, saliva, semen, and vaginal secretions using a quadratic discriminant analysis (QDA) model with 93% accuracy in RNA extracts. Herein, miRNA expression in DNA extracts from 50 donors of each body fluid were tested using the model. Initially, a classification rate of 87% was obtained, which increased to 92% when three additional miRNAs were added. Body fluid identification was found to be reliable across population samples of mixed ages, ethnicities, and sex, with 72-98% of the unknown samples classifying correctly. The model was then tested against compromised samples and over biological cycles, where classification accuracy varied, depending on the body fluid. In conclusion, we demonstrated the ability to classify body fluids using miRNA expression from DNA extracts, eliminating the need for RNA extraction, greatly reducing evidentiary sample consumption and processing time in forensic laboratories, but acknowledge that compromised semen and saliva samples can fail to classify properly, and mixed sample classification remains untested and may have limitations.

Use of ATR-FTIR spectroscopy to differentiate between cirrhotic/non-cirrhotic HCV patients.

BACKGROUND: Conventional techniques to diagnose (HCV) and assess non-cirrhotic/cirrhotic status of the patient for appropriate treatment regime are expensive and invasive. Present available diagnostic tests are expensive as they include multiple screening steps. Therefore, there is a need of cost-effective, less time consuming and minimally invasive alternative diagnostic approaches can be used for effective screening. We propose that (ATR-FTIR) in conjunction with (PCA-LDA),(PCA-QDA) and (SVM) multivariate algorithms can be used as a sensitive tool for detection of HCV infection and to assess non-cirrhotic/cirrhotic status of patients. METHODS: We used 105 sera samples, of which, 55 were from healthy and 50 were from HCV positive individuals. These 50 HCV positive patients were further classified into cirrhotic and non-cirrhotic categories using serum markers and imaging techniques. These samples were freeze dried prior to spectral acquisition then multivariate data classification algorithms were employed to classify these sample types. RESULTS: PCA-LDA and SVM model computed the diagnostic accuracy of 100% for detection of HCV infection. To further classify the non-cirrhotic/cirrhotic status of a patient, diagnostic accuracy of 90.91% for PCA-QDA and 100% for SVM was observed. Internal and external validation for SVM based classifications observed 100% sensitivity and specificity. The confusion matrix generated by PCA-LDA model computed the validation and calibration accuracy showed 100% sensitivity and specificity, by using 2 PCs for HCV infected and healthy individuals. However, when the PCA QDA analysis was done to classify the non-cirrhotic sera samples from cirrhotic sera samples the diagnostic accuracy achieved was 90.91% based on 7 PC's. SVM was also employed for classification and developed model showed the best results with 100% sensitivity and specificity when external validation was applied. CONCLUSIONS: This study provides an initial insight that ATR-FTIR spectroscopy in conjugation with multivariate data classification tools holds a potentialnot onlytoeffectively diagnosis HCV infection but also to assess non-cirrhotic/cirrhotic status of patients.

Sub-Regional Variation and Characteristics of Cabernet Sauvignon Wines in the Eastern Foothills of the Helan Mountain: A Perspective from Phenolics, Visual Properties and Mouthfeel.

As one of the most promising wine regions in China, the eastern foothills of the Helan Mountain (EFHM) in the Ningxia Hui Autonomous Region has attracted great attention recently. Geographically, EFHM is divided into six sub-regions, namely Shizuishan, Xixia, Helan, Qingtongxia, Yongning and Hongsipu. However, there have been few reports on the character and differences between wines in the six sub-regions. In this experiment, a total of 71 commercial Cabernet Sauvignon wines from six sub-regions were collected, and their phenolic compounds, visual properties and mouthfeel were investigated. The results showed that wines from the six sub-regions of EFHM showed distinctive phenolic profiles and could be distinguished through the OPLS-DA mode using 32 potential markers. In terms of color, Shizuishan wines showed higher a* values and lower b* values. The sensory evaluation showed that Hongsipu wines had higher astringency strength and lower tannin texture. The overall results implied that the phenolic compounds of wines in different sub-regions were affected by terroir conditions. To the best of our knowledge, this is the first time that a wide coverage of phenolic compounds has been analysed for wines from the sub-regions of EFHM, which could provide valuable information in deciphering the terroir of EFHM.

Meat hybrids-An assessment of sensorial aspects, consumer acceptance, and nutritional properties.

So-called meat hybrids are a new class of products where a fraction of the meat product (e.g., 20%) is replaced with alternative protein sources, such as plant-based ones. Research suggests that these products could serve as a low-threshold offer for a specific target group that wants to cut down on meat, thereby facilitating the transition toward a more healthy and sustainable diet. Nonetheless, data demonstrate that meat hybrids with a high substantial meat substitution level often fail in the market. This study summarises findings on the physicochemical properties, sensory, and acceptance of six different meat hybrids (70% meat and 30% plant proteins) that were collected in the framework of a case study in the project AiF 196 EN. For this purpose, sensory characteristics were collected via two QDA sessions and a hedonic consumer test. Furthermore, the hybrid recipes were analysed in their proximate composition. The respective recipes varied in protein source (soybean, pumpkin, and pea) and mode of incorporation [textured vegetable protein (TVP), high moisture extrudate (HME)]. It was shown that a meat hybrid with a relatively high share of 30% plant-based proteins with peas as a protein source and TVP as a processing method can still attract consumers.

Detection of terbufos in cases of intoxication by means of entomotoxicological analysis using ATR-FTIR spectroscopy combined with chemometrics.

The detection of toxic substances in larvae from carcasses in an advanced stage of decomposition may help criminal expertise in elucidating the cause of death in suspected cases of poisoning. Terbufos (Counter®) or O,O-diethyl-S-[(tert-butylsulfanyl)methyl] phosphorodithioate is an insecticide and systemic nematicide, which has very high toxicity from an acute point of view (oral LD50 in rodents ranging from 1.4 to 9.2 mg/kg) that has been marketed irregularly and indiscriminately in Brazil as a rodenticide, often being used to practice homicides. The present study aims to evaluate the use of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy to detect traces of terbufos pesticide in fly larvae (Sarcophagidae). ATR-FTIR spectra of scavenger fly larvae from control (n = 31) and intoxicated (n = 80) groups were collected and submitted to chemometric analysis by means of multivariate classification using principal component analysis with quadratic discriminant analysis (PCA-QDA), successive projections algorithm with quadratic discriminant analysis (SPA-QDA) and genetic algorithm with quadratic discriminant analysis (GA-QDA) in order to distinguish between control and intoxicated groups. All discriminant models showed sensitivity and specificity above 90%, with the GA-QDA model showing the best performance with 98.9% sensitivity and specificity. The proposed methodology proved to be sensitive and promising for the detection of terbufos in scavenger fly larvae from intoxicated rat carcasses. In addition, the non-destructive nature of the ATR-FTIR technique may be useful in preserving the forensic evidence, meeting the precepts of the chain of custody and allowing for counter-proof.

The quality of selected raw and pasteurized honeys based on their sensory profiles and consumer preferences.

The purpose of this study was to determine the sensory profile of honeys based on the method of quantitative descriptive analysis and principal component analysis and assess consumer preferences of raw and pasteurized honeys. Samples of multi-floral honeys (from the store and apiary) were subjected to sensory analysis based on the method of ranking for taste preference, the method of scaling based on color, aroma, taste, and texture, and the method of differential descriptive analysis using 11 quality descriptors. The results were subjected to statistical analysis using the Principal Component Analysis method. The taste was found to be a descriptor that differentiates honey by origin. Consumers prefer the taste of pasteurized honeys. As a result of assessing the quality of honeys using the scaling method, it was found that: raw honeys are characterized by a lighter color than pasteurized honeys, store-bought honeys have a less noticeable aroma than honeys obtained from beekeepers, while samples of pasteurized honeys were judged to have a consistency more like that of typical honey. The sensory profiles obtained highlight the differences between pasteurized honeys and raw honeys.

Volatile Profiles and Sensory Characteristics of Cabernet Sauvignon Dry Red Wines in the Sub-Regions of the Eastern Foothills of Ningxia Helan Mountain in China.

To elucidate the effects of the different terroir on wine aroma in six sub-regions of Eastern Foothills of Helan Mountain in Ningxia, a premium wine-producing region in China, 71 Cabernet Sauvignon wines were investigated by gas chromatography-mass spectrometry (GC-MS), check-all-that-apply (CATA), and quantitative descriptive analysis (QDA). The bidirectional orthogonal partial least squares-discriminant analysis (O2PLS-DA) results showed that the Cabernet Sauvignon dry red wines from Xixia (XX) and Yongning (YN) had similar volatile profiles due to their geographical proximity and were characterized by higher concentrations of esters, higher alcohols, and volatile phenols because the similar aromatic profiles were detected in their dry red wines. Shizuishan (SZS) and Hongsipu (HSP) wines showed clear differences compared to the wines of the other four sub-regions, being mainly characterized by relatively higher phenolic aldehydes and volatile phenols. The concentrations of methoxypyrazines and norisoprenoids varied mainly depending on the climate diversity of the sub-regions. The highest 3-isobutyl-2-methoxypyrazine (IBMP) concentration was presented in the Helan (HL) wines. The Qingtongxia (QTX) wines have the highest ß-damascenone, which might be influenced by the fact that QTX has the lowest effective accumulated temperature and the highest sunshine duration among the five sub-regions. Esters including ethyl octanoate, ethyl decanoate, ethyl butanoate, ethyl hexanoate, and isoamyl acetate were the highest in HL. Additionally, the herbaceous, black berry, and red berry notes in HL and QTX were the most outstanding.

OCT-Derived Radiomic Features Predict Anti-VEGF Response and Durability in Neovascular Age-Related Macular Degeneration.

Purpose: No established biomarkers currently exist for therapeutic efficacy and durability of anti-VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability. Design: Assessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy. Participants: Eighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution). Methods: A total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response. Main Outcome Measures: The cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance. Results: The cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained. Conclusions: Radiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.