Partial Least Squares Discriminant Analysis Research Articles

Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections

The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive tools that require minimal expertise and are accessible for routine laboratories. Contrastingly, current conventional diagnostic methods are often found only in specialized environments. Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy offers a nondestructive and user-friendly approach in the analysis of a wide range of samples. In this paper, we determined whether the technique coupled with machine learning can detect and differentiate lymphoma within lymphoid tissue samples. Tissue sections from 295 individuals diagnosed with lymphoma and 389 individuals without the disease were analyzed using ATR-FTIR spectroscopy. The resulting spectral dataset was split using a 70:30 train-test split. Partial least Squares Discriminant Analysis (PLS-DA) models were trained to distinguish non-malignant lymphoid tissue from lymphoma samples and to differentiate between subtypes. On the training set (n = 478), significant spectral differences were mainly identified in the 1800–900 cm–1 region, attributed to fundamental biochemical constituents like proteins, lipids, carbohydrates, and nucleic acids. On the independent test set (n = 206), the trained PLS-DA model achieved a promising AUC of 0.882 (95% CI: 0.881–0.884) in the differentiation between lymphoma and non-malignant lymphoid tissue. In addition, comparative analyses revealed spectral distinctions and notable clustering between the different lymphoma subtypes. This study provides valuable insights into the application of ATR-FTIR spectroscopy and machine learning in the field of lymphoma diagnosis as a non-destructive, rapid and inexpensive tool with the potential to be easily implemented in non-specialized laboratories.

In Vivo Insights: Near-Infrared Photon Sampling of Reflectance Spectra from Cranial and Extracranial Sites in Healthy Individuals and Patients with Essential Tremor

Near-infrared (NIR) spectroscopy is a powerful non-invasive technique for assessing the optical properties of human tissues, capturing spectral signatures that reflect their biochemical and structural characteristics. In this study, we investigated the use of NIR reflectance spectroscopy combined with chemometric analysis to distinguish between patients with Essential Tremor (ET) and healthy individuals. ET is a common movement disorder characterized by involuntary tremors, often making it difficult to clinically differentiate from other neurological conditions. We hypothesized that NIR spectroscopy could reveal unique optical fingerprints that differentiate ET patients from healthy controls, potentially providing an additional diagnostic tool for ET. We collected NIR reflectance spectra from both extracranial (biceps and triceps) and cranial (cerebral cortex and brainstem) sites in ET patients and healthy subjects. Using Partial Least Squares Discriminant Analysis (PLS-DA) and Partial Least Squares (PLS) regression models, we analyzed the optical properties of the tissues and identified significant wavelength peaks associated with spectral differences between the two groups. The chemometric analysis successfully classified subjects based on their spectral profiles, revealing distinct differences in optical properties between cranial and extracranial sites in ET patients compared to healthy controls. Our results suggest that NIR spectroscopy, combined with machine learning algorithms, offers a promising non-invasive method for the in vivo characterization and differentiation of tissues in ET patients.

Characterization of quality parameters and phytosterol content in oils and their formulated margarines

AbstractUnlike lipid stability and oxidation studies in commonly used edible oils and margarines, margarines formulated with unconventional oils are not well characterized. This study investigated the effect of heat treatment (HT) on the stability and content of phytosterol in njangsa seed oil (NSO), bush mango oil (BMO), soybean oil (SBO), coconut oil (CCO), and margarines formulated from their blends: BN (BMO and NSO), BS (BMO and SBO), CN (CCO and NSO), CS (CCO and SBO), and commercial margarines (CM1 and CM2). Both oils and margarines were heat‐treated at 130, 170, and 210°C for 10, 15, 20, and 120 min (only oils). Changes in free fatty acid (FFA), peroxide value (PV), para‐anisidine value (AnV), and fatty acid (FA) composition and phytosterol content were determined after 20 min (margarines) and 120 min (oils) of HT and compared to their control/pre‐HT/unheated (UH) counterparts. The FA composition did not change significantly with HT. Polyunsaturated fatty acids (PUFA)‐rich oils such as NSO and SBO showed significantly higher increase in FFA content with HT compared with oils with higher saturated fatty acid content (BMO and CCO). Oils with higher proportions of linoleic acid, such as SBO (68.3%) and NSO (60.35%), had higher AnV at the end of the HT compared with oils with lower content, such as BMO (0.51%). Phytosterol content fluctuated with HT, and changes in content were generally more pronounced in β‐sitosterol than in stigmasterol and campesterol. Both principal component analysis (PCA) and partial least‐squares discriminant analysis (PLS‐DA) were carried out to observe possible clusters. The results suggest that changes in quality and content of oils and margarines during heating are dependent on more than their fatty acid composition.

Analysis of volatile compound metabolic profiles during the fermentation of filler tobacco leaves through integrated E-nose, GC–MS, GC-IMS, and sensory evaluation

Tobacco is an agricultural commodity with great economic significance. Producers recognize that the flavor characteristics of filler tobacco clear-out (FTLs) affect the quality of cigar tobacco items. To identify the major volatile organic compounds (VOCs) responsible for differences in FTLs, E-nose, HS-SPME-GC–MS, and HS-GC-IMS were employed for multi-dimensional analysis of nine groups of FTLs at various fermentation stages. The E-nose results indicated a progressive increase in nitrogen-oxygen compounds during fermentation. Coupled with these findings, HS-SPME-GC–MS identified 56 VOCs, and HS-GC-IMS revealed 55 VOCs in the FTLs. Principal component analysis (PCA) suggested that FTLs from various fermentation stages of the Yunxue No 39 tobacco plant exhibit significant differences. Partial least squares discriminant analysis (PLS-DA), along with the variable importance in projection (VIP) method for feature selection, pinpointed 12 VOCs as the essential unmistakable metabolites embroiled within the agrarian fermenting prepare of matured FTLs. During the fermentation process, these crucial metabolites are predominantly enriched in pathways including pyruvate metabolism, phenylalanine metabolism, as well as nicotinate and nicotinamide metabolism. The study provides insights into the differences and diversity of FTLs at agricultural fermentation stages. It offers valuable information for optimizing the quality of agricultural fermentation and guiding the directional fermentation of tobacco leaves.

Impact of Mild Field Drought on the Aroma Profile and Metabolic Pathways of Fresh Tea (Camellia sinensis) Leaves Using HS-GC-IMS and HS-SPME-GC-MS

Aroma plays a pivotal role in defining tea quality and distinctiveness, and tea producers have often observed that specific drought conditions are closely associated with the formation and accumulation of characteristic aroma compounds in tea leaves. However, there is still limited understanding of the differential strategies employed by various tea cultivars in response to drought stress for the accumulation of key volatile aroma compounds in fresh tea leaves, as well as the associated metabolic pathways involved in aroma formation. In this study, two widely cultivated tea cultivars in China, Fuding Dabai (FD) and Wuniuzao (WNZ), were examined to assess the impact of mild field drought stress on the composition and accumulation of key volatile aroma compounds in fresh leaves using headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS) and headspace solid phase micro-extraction gas chromatography–mass spectrometry (HS-SPME-GC-MS) technologies. Results revealed that drought stress led to a substantial increase in the diversity of volatile compounds (VOCs) in FD, while WNZ exhibited a notable rise in low-threshold VOC concentrations, amplifying sweet, floral, fruity, and earthy aroma profiles in post-drought fresh leaves. Through partial least squares discriminant analysis (PLS-DA) of HS-GC-IMS and HS-SPME-GC-MS data, integrating variable importance projection (VIP) scores and odor activity values (OAVs) above 1, 9, and 13, key odor-active compounds were identified as potential markers distinguishing the drought responses in the two cultivars. These compounds serve as crucial indicators of the aromatic profile shifts induced by drought, providing insights into the differential metabolic strategies of the cultivars. Additionally, KEGG enrichment analysis revealed 12 metabolic pathways, such as terpenoid biosynthesis, fatty acid synthesis, cutin, suberine, and wax biosynthesis, and phenylalanine metabolism, which may play crucial roles in the formation and accumulation of VOCs in tea leaves under drought stress. These findings provide a comprehensive framework for understanding the cultivar-specific mechanisms of aroma formation and accumulation in tea leaves under mild drought conditions.

Tuning multispectral fluorescence quantum dot–based identification of short-length amyloid β peptides by applying Cu(II) ions

Currently available methods for detecting amyloid β (Aβ) derivatives are mainly dedicated to determining the long forms Aβ1-42 and Aβ1-40. At the same time, the number of physiologically occurring Aβ analogs is much higher, including those truncated at the N- and C-termini. Their identification using standard methods is challenging due to the structural similarity of various Aβ analogs, but could highly benefit from both biomarkers discovery and pathophysiological studies of Alzheimer’s disease. Therefore a “chemical tongue” sensing strategy was employed for the detection of seven Aβ peptide derivatives: Aβ1-16, Aβ4-16, Aβ4-9, Aβ5-16, Aβ5-12, Aβ5-9, Aβ12-16. The proposed sensing system is based on competitive interactions between quantum dots, Cu(II) ions, and Aβ peptides, providing unique fluorescence fingerprints useful for the identification of analytes. After carefully evaluating the Aβ sample preparation protocol, perfect determination of all studied Aβ peptides was achieved using partial least square–discriminant analysis (PLS-DA). The developed PLS-DA models are characterized by excellent accuracy, sensitivity, precision, and specificity of analyte determination, emphasizing the potential of the proposed sensing strategy.Graphical abstract

Comparative metabolomics reveals the mechanism for the high GA4 production in Gibberella fujikuroi CGMCC 17793

With unique advantages, gibberellin GA4 has broad application prospects. To explore the regulatory mechanism for the biosynthesis of GA4, we combined liquid chromatography-mass spectrometry (LC-MS)-based metabolomics with principal component analysis (principal component analysis, PCA) and partial least squares-discriminant analysis (PLS-DA) to screen and identify the differential metabolites between the GA4-producing strains S (industrial high-yield strain CGMCC 17793) and wild-type strain Y (NRRL 13620) of Gibberella fujikuroi fermented for the same time and the differential metabolites of strain S fermented for different time periods. KEGG and MBROLE 2.0 were used to analyze the metabolic pathways involving the differential metabolites. The results showed that compared with strain Y, strain S significantly upregulated and downregulated 107 and 66, 136 and 47, and 94 and 65 metabolites on days 3, 6, and 9, respectively. Compared with that on day 3 of fermentation, strain S upregulated 29 metabolites and downregulated 40 metabolites on day 6 and upregulated 52 metabolites and downregulated 67 metabolites on day 9. The differential metabolites between strain S and strain Y after fermentation for the same time were mainly enriched in amino acid metabolism, tricarboxylic acid (TCA) cycle, and terpenoid biosynthesis. The differential metabolites of strain S after fermentation for different time periods were mainly enriched in amino acid and sugar metabolism pathways. Pathway annotation results indicated that strain S increased the production of acetyl-CoA by promoting amino acid and sugar metabolism and TCA cycle, thereby enhancing the mevalonic acid pathway and increasing the content of isopentenyl pyrophosphate (IPP), a precursor for the synthesis of terpenoids, which ultimately led to increased GA4 production. This study explored the metabolic rules of Gibberella fujikuroi GA4, providing a theoretical basis for regulating Gibberella fujikuroi to improve GA4 production.

Dynamic Analysis of UPLC-MS/MS for Sugar and Organic Acid Components of Pears with Different Flesh Texture Types During Development

Pears are popular among consumers for their juicy and delicious taste. In this study, the sugar and organic acid compositions of pear fruits with different texture types during development were determined by ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS), and fruit texture traits were determined by a texture analyzer. The results showed that the dominant sugar in soft and crispy types of pear fruits was fructose. The main difference between pears was the second-highest sugar component; glucose content was higher in crispy-flesh pear fruits while sucrose content was higher in soft-flesh pear fruits. The composition of organic acid components in both texture types of pear fruits was similar. The turning points of changes in the content of sucrose, sorbitol, glucose and quinic acid were different between different-textured pear varieties. A Pearson correlation analysis showed that sugar and organic acid components were significantly correlated with single fruit weight and soluble solid contents (SSCs), respectively. There was a high correlation among texture traits, individual sugars and organic acids. A partial least squares discriminant analysis (PLS-DA) VIP score plot showed that the differential traits with scores greater than 1 were total soluble sugars/total organic acids (TSSs/TAs), fracture and malic acid/citric acid (MA/CA), which could distinguish pear fruits of different texture types better and reflect the uneven quality differences among pear fruits adapted to different origins. The comprehensive analysis results of the flesh texture parameters and sugar and organic acid components are in line with objective reality and will provide a reference for quantitative indicators of the sensory evaluation of pear varieties as well as for molecular mechanism research on trait differences.

Terminalia ivorensis demonstrates antioxidant properties and alters proliferation, genomic instability and migration of human colon cancer cells in vitro.

Colorectal cancer is a global killer that causes approximately 940 thousand deaths annually. Terminalia ivorensis (TI) is a tropical tree, the bark of which is used in African traditional medicine for the treatment of diabetes, malaria and ulcer. This study investigated TI as a potential anticancer agent in human colon cells in vitro. TI was extracted sequentially with petroleum ether, chloroform, ethyl acetate and ethanol. Antioxidant activity was assessed by DPPH and FRAP, and differential effects on cell viability, growth, DNA damage, DNA repair, and migration were measured in human colon cancer cells (CaCo-2) and/or non-cancerous human colonocytes (NCM460). The TI phytochemicals most strongly associated with these effects were identified by partial least-squares discriminant analysis. DPPH and FRAP activity were highest in TI ethyl acetate and ethanol extracts (p=0.001). All TI extracts significantly inhibited cell viability and growth and induced DNA damage and inhibited DNA repair in both cell models. The majority of TI extracts were significantly (p=0.01) more toxic to cancer cells than non-cancerous colonocytes. DNA repair was significantly (p=0.001) inhibited in CaCo-2 cells by ethyl acetate extract compared with NCM460 cells. Migration was also significantly inhibited (p<0.001) in CaCo-2 by ethyl acetate (80%) and ethanol extracts (75%). Specific benzoic acids, flavonoids and phenols were identified to be strongly associated with these effects. TI displayed strong antioxidant activity and specific anticancer effects by inducing cell death and DNA damage, and by inhibiting DNA repair, cell proliferation and migration.

Early Metabolomic Profiling as a Predictor of Renal Function Six Months After Kidney Transplantation

Background: Kidney transplantation is the therapy of choice for patients with advanced chronic kidney disease; however, predicting graft outcomes remains a significant challenge. Early identification of reliable biomarkers could enhance post-transplant management and improve long-term outcomes. This study aimed to identify metabolomic biomarkers within the first week after kidney transplantation that predict renal function at six months. Methods: We conducted a prospective study involving 50 adult patients who received deceased donor kidney transplants. Plasma samples collected one week after transplant were analyzed using liquid chromatography–mass spectrometry in a semi-targeted metabolomic approach. A Partial Least Squares-Discriminant Analysis (PLS-DA) model identified metabolites associated with serum creatinine &gt; 1.5 mg/dL at six months. Metabolites were selected based on a Variable Importance in Projection (VIP) score &gt; 1.5, which was used to optimize model performance. Results: The PLS-DA model demonstrated strong predictive performance with an area under the curve (AUC) of 0.958. The metabolites negatively associated with serum creatinine &gt; 1.5 mg/dL were 3-methylindole, guaiacol, histidine, 3-indolepropionic acid, and α-lipoic acid. Conversely, the metabolites positively associated with worse kidney graft outcomes included homocarnosine, 5-methylcytosine, xanthosine, choline, phenylalanine, kynurenic acid, and L-kynurenine. Conclusions: Early metabolomic profiling after transplantation shows promise in predicting renal function. Identifying metabolites with antioxidant and anti-inflammatory properties, as well as those that are harmful and could be targeted therapeutically, underscores their potential clinical significance. The link between several metabolites and the tryptophan pathway suggests that further specific evaluation of this pathway is warranted. These biomarkers can enhance patient management and graft survival.

Metabolomic signature of sperm in men with obesity-associated asthenozoospermia.

Obese men have a significantly increased risk of developing asthenozoospermia. Sperm motility is directly related to cellular energy supply and metabolic status. Sperm metabolomics research based on Gas chromatography-mass spectrometry (GC-MS) technology can provide useful information for the pathological mechanism, diagnosis, and treatment of obesity-associated asthenozoospermia. Sperm samples were obtained from a healthy control group (n = 49) and patients with obesity-associated asthenozoospermia (n = 40). After the analysis of sperm samples using GC-MS, various multivariate statistical methods such as principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) were conducted. A total of 56 metabolites were identified in the sperm samples. Among them, 19 differential metabolites were found between the two groups. Metabolites such as glutamic acid, fumaric acid, and cysteine were significantly downregulated in the sperm of patients with obesity-associated asthenozoospermia, while metabolites like palmitic acid, stearic acid, and alanine were significantly upregulated. The differential metabolites were enriched in D-glutamine and D-glutamate metabolism; proline, aspartate, and glutamate metabolism; glutathione metabolism and the other metabolic pathways. Obesity may influence the composition of metabolic products in sperm, and metabolomic analysis proves beneficial for the future diagnosis and treatment of obesity-associated asthenozoospermia.

Decoding wheat contamination through self-assembled whole-cell biosensor combined with linear and non-linear machine learning algorithms

The contamination of mycotoxins is a serious problem around the world. It has detrimental effects on human beings and leads to tremendous economic loss. It is essential to develop a rapid and non-destructive method for contamination recognition particularly for early alarm. In this study, the whole-cell biosensor array was constructed and employed for rapid recognition of wheat contamination by combining with machine learning algorithms. Seven key VOCs were explored through univariate coupling to multivariate analysis of orthogonal partial least squares-discrimination analysis (OPLS-DA) models. The promoters of dnaK, katG, oxyR, soxS obtained from the stress-responsive of key VOCs were fused to the bacterial operon and fabricated on the whole-cell biosensor. The constructed whole-cell biosensor array was consisted with four kinds of sensors and 18 sensor unit. The bioluminescent intensity combined with linear machine learning algorithm of partial least squares discriminant analysis (PLS-DA) and non-linear algorithms of back propagating artificial neural network (BP-ANN) and least square support vector machine (LS-SVM) were employed to establish discrimination models for mold contamination especially for early warning. The Monte-Carlo strategy was performed to generate thirty subsets for modeling to give more reliable results. As a result, the whole-cell biosensor combined with non-linear algorithm of LS-SVM was practicable for detecting mold identification for wheat early-warning with the accuracy of 97.24%. Additionally, this study provides practical and effective methods not only for wheat quality guarantee and supervision but also for other foodstuffs.

Effects of Helicobacter pylori and Helicobacter pylori eradication on the microbiota of tongue coating

Eradicating Helicobacter pylori (H. pylori) can cause an imbalance in the microbiota. Dysbiosis of the gut microbiome may produce multiple diseases and bacterial infections. The objective of this study was to investigate the influence of Helicobacter pylori (H. pylori) infection and its eradication on the composition of the oral tongue coating microbiota. A cohort of 35 participants was recruited and categorized into two groups: the H. pylori negative group (N group) consisting of 12 individuals and the H. pylori positive group (23 individuals). Within the H. pylori positive group, subjects were further stratified into the H. pylori pre-eradicated group (HPQ group) and the H. pylori eradicated group (HPH group). H. pylori positive individuals were treated with a quadruple regimen containing bismuth, and tongue coating samples were collected both prior to and following treatment. Concurrently, tongue coating samples were collected from H. pylori negative individuals. High-throughput 16S rRNA sequencing technology was employed to assess the microbial composition of the tongue coating in the N group, HPQ group, and HPH group. Pertinent clinical data were documented.Microbial diversity was found to significantly differ among the N group, HPQ group, and HPH group, as evidenced by variations in Chao1 index, Shannon index, and Partial Least Squares Discriminant Analysis (PLS-DA). The dominant bacterial phyla identified across all groups included Bacteroidetes, Proteobacteria, Firmicutes, Fusobacteria, Actinobacteria, and Saccharibacteria. At the phylum level, Firmicutes exhibited higher relative abundance in the HPQ group in comparison to both the N group and HPH group. Conversely, Bacteroidetes displayed greater prevalence in the N group and HPH group. Linear Discriminant Analysis Effect Size (LEfSe) analysis indicated a higher abundance of Romboutsia, Rothia, and Turiciactor in the HPQ group. Our study revealed significant disparities in microbial diversity and richness among the three groups. Furthermore, our findings suggest a potential association between the presence of Streptococcus, Rothia and H. pylori positive individuals.

Identification of potential volatile markers for characterizing Argentine wine vinegars based on their production process

In Argentina, the production of quality wine vinegars has been barely exploited. Currently, most of the marketed vinegars are produced using rapid industrial fermentation systems obtaining vinegars with a high production rate, but with low quality and few organoleptic nuances. This work aims to study the volatile profile by headspace solid phase microextraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GC–MS) and chemometrics to differentiate Argentinian wine vinegars according to their production process (industrial or traditional). A total of 92 volatile compounds were identified on the samples by using a strategy based on volatile profile processing using PARADISe® software. The complete volatile profile of all the samples was submitted to partial least squares discriminant analysis (PLS-DA) for the selection of variables with importance in the projection (VIPs). Thus, 37 volatile compounds with the potential to be markers of the manufacturing process were selected. The results obtained revealed, for the first time, the volatile profile of Argentine wine vinegars showing that the compound groups that, on average, exhibited higher relative areas were esters, acids, and alcohols. Thus, certain acids, aldehydes and terpenes were noticeably present in industrial wine vinegar. Conversely, the alcohols were strongly associated with traditional ones. In the case of esters, they were connected to the different types of wine vinegars. These compounds could be considered as potential volatile markers of quality and authenticity of these types of vinegars.

P10.22.B PREDICTING INTRAOPERATIVE 5-ALA-INDUCED TUMOR FLUORESCENCE VIA MRI AND DEEP LEARNING IN GLIOMAS WITH RADIOGRAPHIC LOWER-GRADE CHARACTERISTICS

Abstract BACKGROUND Lower-grade gliomas typically exhibit 5-aminolevulinic acid (5-ALA)-induced fluorescence in only 20-30% of cases, a rate that can be increased by doubling the administered dose of 5-ALA. Fluorescence can depict anaplastic foci, which can be precisely resected to avoid undergrading. We aimed to analyze whether a deep learning model can predict intraoperative fluorescence based on preoperative magnetic resonance imaging (MRI). Material and Methods: The MRI images of gliomas lacking high-grade characteristics (necrosis, extended contrast-enhancement, a.o.) consisted of T1, T1-post gadolinium, and FLAIR sequences. The preprocessed MRIs were fed into an encoder-decoder convolutional neural network (U-Net), pre-trained for tumor segmentation using those three MRI sequences. We used the outputs of the bottleneck layer of the U-Net in the Variational Autoencoder (VAE) as features for classification. We identified and utilized the most effective features in a Random Forest classifier using the principal component analysis (PCA) and the partial least square discriminant analysis (PLS-DA) algorithms. We evaluated the performance of the classifier using a 10-fold cross-validation procedure. RESULTS We evaluated a cohort of 163 glioma patients categorized as fluorescent (n=83) or non-fluorescent (n=80). Our proposed approach’s performance was evaluated using metrics such as mean balanced accuracy, mean sensitivity, and mean specificity. The optimal results were obtained by employing top-performing features selected by PCA, resulting in a mean balanced accuracy of 80% and mean sensitivity and specificity of 84% and 76%, respectively. CONCLUSION Our findings highlight the potential of a U-Net model, coupled with a random forest classifier, for intraoperative fluorescence prediction. We achieved good accuracy using advanced techniques such as deep learning-based tumor segmentation and Variational Autoencoder for radiomics feature extraction. While the model can still be improved, it has the potential for evaluating when to administer 5-ALA to gliomas lacking typical high-grade radiographic features.

The Effect of Temperature and Humidity on Yellow Tea Volatile Compounds during Yellowing Process.

Yellowing is the key processing technology of yellow tea, and environmental conditions have a significant impact on the yellowing process. In this study, volatile compounds of the yellowing process under different environmental conditions were analyzed by GC-MS. Results showed that a total of 75 volatile compounds were identified. A partial least squares discriminant analysis (PLS-DA) determined that 42 of them were differential compounds, including 12 hydrocarbons, 8 ketones, 8 aldehydes, 6 alcohols, and 8 other compounds, and compared the contents of differential compounds under the conditions of 40 °C with 90% humidity, 50 °C with 50% humidity, and 30 °C with 70% humidity, then analyzed the variation patterns of hydrocarbons under different yellowing environmental conditions. A 40 °C with 90% humidity treatment reduced the content of more hydrocarbons and increased the aldehydes. The content of 3-hexen-1-ol was higher when treated at 50 °C with 50% humidity and was consistent with the results of sensory evaluation. This study could provide a theoretical basis for future research on the aroma of yellow tea.

Unrevealing the Halyomorpha halys Damage Fingerprint on Hazelnut Metabolome by Multiomic Platforms and AI-Aided Strategies.

The brown marmorated stink bug (Halyomorpha halys) poses a significant threat to hazelnut crops by affecting kernel development and causing quality defects, reducing the market value. While previous studies have identified bitter-tasting compounds in affected kernels, the impact of stink bug feeding on the hazelnut metabolome, particularly concerning aroma precursors, remains underexplored. This study aims to map the nonvolatile metabolome and volatilome of hazelnut samples obtained by caging H. halys on different cultivars in two locations to identify markers for diagnosing stink bug damage. Using a multiomic approach involving headspace solid-phase microextraction (HS-SPME), comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOF MS), and liquid chromatography-high-resolution mass spectrometry (LC-HRMS), both raw and roasted hazelnuts are analyzed, with artificial intelligence (AI) and machine learning tools employed to explore data correlations. The study finds that the hazelnut metabolome and volatilome exhibit high chemical complexity with significant classes of compounds such as aldehydes, ketones, alcohols, and terpenes identified in both raw and roasted hazelnuts. Multivariate analysis indicates that the orchard location significantly impacts the metabolome, followed by damage type, with cultivar differences being less pronounced. Partial least-squares discriminant analysis (PLS-DA) models achieve high predictive accuracy for orchard location (99%) and damage type (≈80%), with the roasted volatilome showing the highest predictive accuracy. Correlation matrices reveal significant relationships between raw hazelnut metabolites and aroma compounds in roasted samples, suggesting potential markers for stink bug damage that could guide the quality assessment and mitigation strategies. Data fusion techniques further enhance classification performance, particularly in predicting damage type, underscoring the potential of integrating multiple data sets for comprehensive quality assessment.

Machine Learning Classification of Integrin-Expression-Based Magnetic Sorted SW 620 Cells by Simultaneous O-PTIR and SERS.

Immortalized cell lines are commonly used for in vitro studies such as drug efficacy, toxicology, and life cycle due to their cost effectiveness and accessibility; however, subpopulations within a cell line can arise from random mutations or asynchronous cell cycles which may lead to results that make interpretation difficult. A method that could classify these differences and separate unique subpopulations would increase our understanding of heterogeneous cellular responses. In the present work, we explore spectroscopic signals associated with subpopulations of cells magnetically sorted on the basis of α5β1 integrin binding to cyclic-RGDfC which mimics fibronectin in the extracellular matrix. SW620 colon cancer cells were incubated with cyclic-RGDfC functionalized gold-coated, iron core nanoparticles and magnetically sorted. The subpopulations from the sort were imaged (N = 10 positive and N = 10 negative, number of cells) via simultaneous surface-enhanced Raman scattering (SERS) and optical-photothermal infrared spectroscopy (O-PTIR). Pearson correlations of the standard peptide-protein interaction in the SERS channel allowed for visualization of the cyclic RGDfC-integrin α5β1 interaction. Partial least-squares discriminant analysis of the O-PTIR spectra collected from cell maps successfully classified the positively or negatively sorted cells. These results demonstrate that biochemical changes within a single cell line can be sorted via an integrin-activity-based assay using simultaneous SERS and O-PTIR.

Rapid determination of fat content: Advanced spectroscopic methods across diverse fish species

The rapid estimation of fat content using spectroscopic-based sensors, irrespective of fish species (salmon, trout, sea bass, sea bream, tuna, cod, and mackerel), was primarily investigated in the present study. The fat content and fatty acid (FA) composition was quantified with reference methods. Fourier-Transform Infrared (FTIR) and Fourier-Transform Near-Infrared (FT-NIR) spectroscopy, along with multispectral imaging (MSI) instruments (both benchtop and portable), were evaluated for their ability to rapidly predict fat content in ground samples using data analysis. The performance of the PLS-R models was evaluated according to root mean square error (RMSE), coefficient of determination (R2) and residual prediction deviation (RPD). Also fish were classified as fat/low-fat and low/medium/high-fat using partial least squares discriminant analysis (PLS-DA). All fish species exhibited a consistent pattern of unsaturated fatty acids (UNFA) > monounsaturated fatty acids (MUFA) > polyunsaturated fatty acids (PUFA) > saturated fatty acids (SFA) and the range of fat content was from 22.8 (salmon) to 0.02 (cod), expressed in g/100 g of sample based on the reference methods. In terms of fat content prediction using rapid sensors the best performance indices of the test set were obtained from the benchtop-MSI (RMSE = 1.475, R2 = 0.847, RPD = 2.581) and FT-NIR (RMSE = 1.638, R2 = 0.855, RPD = 2.651) instruments, while FTIR and portable-MSI had scores of RMSE = 1.874, R2 = 0.815, RPD = 2.309 and RMSE = 1.737, R2 = 0.786, RPD = 2.191, respectively. All sensors discriminated fat from low-fat samples (accuracy = 100%). For low/medium/high fat the best results were achieved by benchtop-MSI (accuracy = 94.12%), followed by portable-MSI (accuracy = 88.24%) and FTIR (accuracy = 84.21%). Results were less satisfactory for the FT-NIR method (accuracy = 68.75%). This study demonstrates that vibrational and multispectral imaging spectroscopies, when coupled with data analysis, have potential to provide information about the nutritional quality (e.g., fat content) independent of the fish species. Such insights could contribute to the automation and potential digitalization of processes within the fields of food nutrition and the seafood industry.

Attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) analysis of human nails: Implications for age determination in forensics.

A person's age estimation from biological evidence is a crucial aspect of forensic investigations, aiding in victim identification and criminal profiling. In this study, we present a novel approach of utilizing Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) spectroscopy to predict the age of donors based on nail samples. A diverse dataset comprising nails from donors spanning different age groups was analyzed using ATR FT-IR, with subsequent multivariate analysis techniques used for age prediction. The developed partial least squares regression (PLS-R) model demonstrated promising accuracy in age estimation, with a root mean square error of prediction (RMSEP) equal to 11.1 during external validation. Additionally, a partial least squares discriminant analysis (PLS-DA) classification model achieved high accuracy of 88% in classifying donors into younger and older age groups during external validation. This proof-of-concept study highlights the potential of ATR FT-IR spectroscopy as a non-destructive and efficient tool for age estimation in forensic investigations, offering a new approach to forensic analysis with practical implications.