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The genus Hordeum (barley) represents an essential group within the Poaceae family, comprising diverse species with significant ecological and economic importance. This study aims to improve the infrageneric classification of Hordeum by integrating multiple analytical approaches based on morphological data. A comprehensive dataset of key morphological traits was compiled from a wide range of Hordeum accessions, including representatives from all major taxonomic groups within the genus. Understanding and classifying the evolutionary traits of barley species, particularly in terms of environmental adaptation, pest resistance, and productivity improvement, is essential. DNA-based classification methods allow precise molecular-level analysis but are resource-intensive, especially when large-scale processing is required. This study addresses these limitations by employing an integrative approach combining hierarchical clustering, Principal Component Analysis–Linear Discriminant Analysis (PCA-LDA), and Random Forest (RF) to analyze the compiled morphological datasets. Morphological clustering via hierarchical analysis revealed clear taxonomic distinctions, achieving 86.0% accuracy at the subgenus level and 83.1% at the section level. PCA-LDA further refined classification by identifying key traits such as seed width, area, and 100-seed weight as primary contributors, achieving perfect accuracy for the Hordeum section and high accuracy for species like Hordeum vulgare and Hordeum spontaneum. RF analysis enhanced classification performance, achieving 100% accuracy at the section level and high accuracy for species with sufficient data. This approach offers a new framework for classifying diverse barley species and contributes significantly to data-driven decision-making in breeding and conservation efforts, supporting a deeper understanding of barley’s adaptive evolution in response to environmental changes.

To fight counterfeits, and to protect the consumer, the interest in certifying the origin of agricultural goods has been growing in recent years. In this context and to increase the accuracy of zoning models, multiple analytical techniques must be combined via a multivariate approach. During the sampling campaign, leaves and fruits (olives or drupes) were collected from multiple orchards and farms. By means of HPLC-DAD, metabolite levels were evaluated and combined with the trace and ultra-trace metal/metalloid levels evaluated by ICP-MS (QqQ). The combined dataset was then used to develop a model for geographical traceability. Furthermore, the mineral content of the soil, evaluated by means of ICP-MS, was correlated with both the mineral content in the leaves and drupes and the metabolomic profiles to further investigate the connection between the orchard’s location and characteristics of the final products.

Pathogen infection can damage agricultural products, thereby reducing their economic value. Fusarium spp. is a fungal pathogen that infects potatoes (Solanum tuberosum L.) and causes dry rot. In this study, we utilized visible–near-infrared (Vis–NIR) and shortwave–near-infrared (SW–NIR) spectroscopy for the early detection of Fusarium spp. infection in potato tubers. The spectrometer used in this study analyzed the Vis–NIR (400–1,000 nm) and SW–NIR (970–1,700 nm) regions. A total of 183 potato (G2 “Granola L.” variety) samples were used. Among these, 93 samples were artificially inoculated with Fusarium solani mycelia, while 90 samples were left uninfected and considered the control group. The potato samples were stored at two different temperatures (12 and 25°C). Vis–NIR and SW–NIR spectra were analyzed by a chemometric method, namely principal component analysis with linear discriminant analysis (PCA–LDA), to differentiate healthy and infected potatoes. The PCA–LDA model based on Vis–NIR spectra exhibited a calibration accuracy of 80.26% and a reliability of 65%. Meanwhile, the PCA–LDA model based on SW–NIR spectra exhibited a calibration accuracy of 100% and a reliability of 97.30%. Overall, both methods demonstrated their suitability for differentiating potato tubers with Fusarium spp. fungal infection and healthy ones; however, the results suggest that SW–NIR spectroscopy is more effective than Vis–NIR spectroscopy.

For several years, Fourier transform infrared (FTIR) microspectroscopy has been proving to be very promising for use in cytological diagnostics because of its capability of providing rapid and label-free biochemical information about cell samples. The adoption of FTIR as a clinical tool has been slowed because of the poor compatibility with cells deposited on glass slides, commonly used in clinical practice, because of the absorption of IR radiation by glassy materials in the 1000–1800 cm−1 spectral range. However, the possibility of also obtaining diagnostic information from the IR absorption spectra in the 2700–3700 cm−1 range (including few peaks related to vibrational modes in cell lipids and proteins) has recently emerged. In this work, we investigate the use of the FTIR technique in the 2700–3700 cm−1 range for diagnostic purposes about human colon cells grown on glass coverslips. In fact, using the principal components analysis (PCA) technique, we are able to discriminate FTIR spectra of healthy cells from those of cancerous ones, mainly due to the larger relative lipid content in the former compared to the latter. In addition, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were used to build classification models for unknown FTIR spectra with optimal accuracy. These results support the promotion of the translation of the FTIR technique as a complementary diagnostic tool in cytological routine practice.

Label-free surface-enhanced Raman scattering (SERS) analysis shows tremendous potential for the early diagnosis and screening of colon cancer, owing to the advantage of being noninvasive and sensitive. As a clinical diagnostic tool, however, the reproducibility of analytical methods is a priority. Herein, we successfully fabricated Ag NPs/cellulose nanocrystals/graphene oxide (Ag NPs/CNC/GO) nanocomposite film as a uniform SERS active substrate for label-free SERS analysis of clinical serum. The Ag NPs/CNC/GO suspensions by self-assembling GO into CNC solution through in-situ reduction method. Furthermore, we spin-coated the prepared suspensions on the bacterial cellulose membrane (BCM) to form Ag NPs/CNC/GO nanocomposite film. The nanofilm showed excellent sensitivity (LOD = 30 nM) and uniformity (RSD = 14.2%) for Nile Blue A detection. With a proof-of-concept demonstration for the label-free analysis of serum, the nanofilm combined with the principal component analysis-linear discriminant analysis (PCA-LDA) model can be effectively employed for colon cancer screening. The results showed that our model had an overall prediction accuracy of 84.1% for colon cancer (n = 28) and the normal (n = 28), and the specificity and sensitivity were 89.3% and 71.4%, respectively. This study indicated that label-free serum SERS analysis based on Ag NPs/CNC/GO nanocomposite film combined with machine learning holds promise for the early diagnosis of colon cancer.

The emergence of paraffin-coated rice in China, aimed at enhancing its market appeal and achieving a translucent appearance, has given rise to a significant global food safety concern. This situation poses substantial health risks to consumers. Hyperspectral analysis, recognized as a powerful and nondestructive technique for assessing food quality and safety, offers a potential solution. This study conducted a comprehensive investigation using Visible-Near Infrared (VIS-NIR) hyperspectral imaging systems operating within the 400-1000 nm range to identify paraffin-contaminated rice. Various rice varieties from diverse regions were obtained and intentionally tainted with varying levels of paraffin. Imaged samples were further preprocessed for spectral data extraction from individual rice seeds’ regions of interest (ROI). The dataset encompassed 3000 spectral records obtained from both non-contaminated and contaminated samples. The obtained spectral data were employed to develop partial least squares discriminant analysis (PLS-DA) and principal component linear discriminant analysis. The primary goal was to discriminate between contaminated and non-contaminated rice samples effectively. Notably, the results indicated that PLS-DA consistently achieved an accuracy exceeding 94% across various preprocessing techniques. Overall, this study showcased the potential of combining hyperspectral imaging with chemometrics to detect paraffin-contaminated rice seeds, providing a valuable contribution to food safety assessment in the industry.

Choosing the cacao maturity stage is essential for producing high-quality cacao beans. Identifying indicators of the maturity level of cacao is a complex task because these fruits do not exhibit the characteristics of other fruits during the ripening period. Generally, cacao maturity is determined manually based on the estimated daily harvest date using sensory observation, which is marked by changes in the color of the cacao skin. This is certainly inaccurate because visual assessment is only performed subjectively. This is inaccurate because visual assessment is only performed subjectively, which is not in line with the demands of Industrial Revolution 4.0, which is a fast and accurate technology for sorting cacao. In this study, cacao maturity was identified using visible (350-1000 nm) and shortwave near-infrared spectra (SW-NIR) spectroscopy (1000-1600 nm). Chemometric analysis using principal component analysis-linear discriminant analysis (PCA-LDA) was used to classify cacao maturity. The results showed that SW-NIR spectroscopy yielded better performances with calibration and prediction accuracy of 92,50% and 85% using Savitzky–s 1st derivative (SGD1) spectra compared to Vis-NIR spectroscopy had calibration and prediction accuracies of 90% and 86% using raw spectra for PCA-LDA model.

Various methods for detecting malaria have been developed in recent years, each with its own set of advantages. These methods include microscopic, antigen-based, and molecular-based analysis of blood samples. This study aimed to develop a new, alternative procedure for clinical use by using a large data set of surface-enhanced Raman spectra to distinguish normal and infected red blood cells. PCA-LDA algorithms were used to produce models for separating P. falciparum (3D7)-infected red blood cells and normal red blood cells based on their Raman spectra. Both average normalized spectra and spectral imaging were considered. However, these initial spectra could hardly differentiate normal cells from the infected cells. Then, discrimination analysis was applied to assist in the classification and visualization of the different spectral data sets. The results showed a clear separation in the PCA-LDA coordinate. A blind test was also carried out to evaluate the efficiency of the PCA-LDA separation model and achieved a prediction accuracy of up to 80%. Considering that the PCA-LDA separation accuracy will improve when a larger set of training data is incorporated into the existing database, the proposed method could be highly effective for the identification of malaria-infected red blood cells.

Vibrational spectroscopies provide information about the biochemical and structural environment of molecular functional groups inside samples. Over the past few decades, Raman and infrared-absorption-based techniques have been extensively used to investigate biological materials under different pathological conditions. Interesting results have been obtained, so these techniques have been proposed for use in a clinical setting for diagnostic purposes, as complementary tools to conventional cytological and histological techniques. In most cases, the differences between vibrational spectra measured for healthy and diseased samples are small, even if these small differences could contain useful information to be used in the diagnostic field. Therefore, the interpretation of the results requires the use of analysis techniques able to highlight the minimal spectral variations that characterize a dataset of measurements acquired on healthy samples from a dataset of measurements relating to samples in which a pathology occurs. Multivariate analysis techniques, which can handle large datasets and explore spectral information simultaneously, are suitable for this purpose. In the present study, two multivariate statistical techniques, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were used to analyse three different datasets of vibrational spectra, each one including spectra of two different classes: (i) a simulated dataset comprising control-like and exposed-like spectra, (ii) a dataset of Raman spectra measured for control and proton beam-exposed MCF10A breast cells and (iii) a dataset of FTIR spectra measured for malignant non-metastatic MCF7 and metastatic MDA-MB-231 breast cancer cells. Both PCA-LDA and PLS-DA techniques were first used to build a discrimination model by using calibration sets of spectra extracted from the three datasets. Then, the classification performance was established by using test sets of unknown spectra. The achieved results point out that the built classification models were able to distinguish the different spectra types with accuracy between 93% and 100%, sensitivity between 86% and 100% and specificity between 90% and 100%. The present study confirms that vibrational spectroscopy combined with multivariate analysis techniques has considerable potential for establishing reliable diagnostic models.

The quality of probiotics has been associated with bacteria and yeast strains’ contents and their stability against conditioning factors. Near-infrared spectroscopy (NIRS), as a non-destructive, fast, real-time, and cost-effective analytical technique, can provide some advantages over more traditional food quality control methods in quality evaluation. The aim of our study was to evaluate the applicability of NIRS to the characterization and viability prediction of three commercial probiotic food supplement powders containing lactic acid bacteria (LAB) subjected to concentration and temperature conditioning factors. For each probiotic, 3 different concentrations were considered, and besides normal preparation (25 °C, control), samples were subjected to heat treatment at 60 or 90 °C and left to cool down until reaching room temperature prior to further analysis. Overall, after applying chemometrics to the NIR spectra, the obtained principal component analysis-based linear discriminant analysis (PCA-LDA) classification models showed a high accuracy in both recognition and prediction. The temperature has an important impact on the discrimination of samples. According to the concentration, the best models were identified for the 90 °C temperature treatment, reaching 100% average correct classification for recognition and over 90% for prediction. However, the prediction accuracy decreased substantially at lower temperatures. For the 25 °C temperature treatment, the prediction accuracy decreased to nearly 60% for 2 of the 3 probiotics. Moreover, according to the temperature level, both the recognition and prediction accuracies were close to 100%. Additionally, the partial least square regression (PLSR) model achieved respectable values for the prediction of the colony-forming units (log CFU/g) of the probiotic samples, with a determination coefficient for prediction (R2Pr) of 0.82 and root mean square error for prediction (RMSEP) of 0.64. The results of our study show that NIRS is a fast, reliable, and promising alternative to the conventional microbiology technique for the characterization and prediction of the viability of probiotic supplement drink preparations.

Na Chen,1 Ming Rong,1 Xiaoguang Shao,2 Heng Zhang,1 Shupeng Liu,1,3 Baijun Dong,2 Wei Xue,2 Tingyun Wang,1 Taihao Li,3 Jiahua Pan2 1Key Laboratory of Specialty Fiber Optics and Optical Access Networks, School of Communication and Information Engineering, Shanghai University, 2Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 3Beijing Advanced Innovation Center for Imaging Technology, Capital Normal University, Beijing, People’s Republic of China Abstract: The surface-enhanced Raman spectroscopy (SERS) of blood serum was investigated to differentiate between prostate cancer (PCa) and benign prostatic hyperplasia (BPH) in males with a prostate-specific antigen level of 4–10 ng/mL, so as to reduce unnecessary biopsies. A total of 240 SERS spectra from blood serum were acquired from 40 PCa subjects and 40 BPH subjects who had all received prostate biopsies and were given a pathological diagnosis. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA) diagnostic algorithms, were used to analyze the spectra data of serum from patients in control (CTR), PCa and BPH groups; results offered a sensitivity of 97.5%, a specificity of 100.0%, a precision of 100.0% and an accuracy of 99.2% for CTR; a sensitivity of 90.0%, a specificity of 97.5%, a precision of 94.7% and an accuracy of 98.3% for BPH; a sensitivity of 95.0%, a specificity of 93.8%, a precision of 88.4% and an accuracy of 94.2% for PCa. Similarly, this technique can significantly differentiate low- and high-risk PCa with an accuracy of 92.3%, a specificity of 95% and a sensitivity of 89.5%. The results suggest that analyzing blood serum using SERS combined with PCA–LDA diagnostic algorithms is a promising clinical tool for PCa diagnosis and assessment. Keywords: Ag nanoparticles, linear discriminant analysis, gray zone, principle component analysis, benign prostatic hyperplasia, spectral classification

The postharvest processing factors including cherry processing methods highly influence the final quality of coffee beverages, especially in the composition of several coffee metabolites such as glucose, fructose, the amino acid (glutamic acid), and chlorogenic acids (CGA) as well as trigonelline contents. In this research, UV spectroscopy combined with chemometrics was used to classify a ground roasted Lampung robusta specialty coffee according to differences in the cherry processing methods. A total of 360 samples of Lampung robusta specialty coffee with 1 g of weight for each sample from three different cherry processing methods were prepared as samples: 100 samples of pure dry coffee (DRY), 100 samples of pure semi-dry coffee (SMD), 100 samples of pure wet coffee (WET) and 60 samples of adulterated coffee (ADT) (SMD coffee was adulterated with DRY and WET coffee). All samples were extracted using a standard protocol as explained by previous works. A low-cost benchtop UV-visible spectrometer (Genesys™ 10S UV-Vis, Thermo Scientific, Waltham, MA, USA) was utilized to obtain UV spectral data in the interval of 190–400 nm using the fast scanning mode. Using the first three principal components (PCs) with a total of 93% of explained variance, there was a clear separation between samples. The samples were clustered into four possible groups according to differences in cherry processing methods: dry, semi-dry, wet, and adulterated. Four supervised classification methods, partial least squares–discriminant analysis (PLS-DA), principal component analysis–linear discriminant analysis (PCA-LDA), linear discriminant analysis (LDA) and support vector machine classification (SVMC) were selected to classify the Lampung robusta specialty coffee according to differences in the cherry processing methods. PCA-LDA is the best classification method with 91.7% classification accuracy in prediction. PLS-DA, LDA and SVMC give an accuracy of 56.7%, 80.0% and 85.0%, respectively. The present research suggested that UV spectroscopy combining with chemometrics will be highly useful in Lampung robusta specialty coffee authentication.

Breast cancer is one of the major cancers of women in the world. Despite significant progress in its treatment, an early diagnosis can effectively reduce its incidence rate and mortality. To improve the reliability of Raman-based tumor detection and analysis methods, we conducted an ex vivo study to unveil the compositional features of healthy control (HC), solid papillary carcinoma (SPC), mucinous carcinoma (MC), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC) tissue samples. Following the identification of biological variations occurring as a result of cancer invasion, principal component analysis followed by linear discriminate analysis (PCA-LDA) algorithm were adopted to distinguish spectral variations among different breast tissue groups. The achieved results confirmed that after training, the constructed classification model combined with the leave-one-out cross-validation (LOOCV) method was able to distinguish the different breast tissue types with 100% overall accuracy. The present study demonstrates that Raman spectroscopy combined with multivariate analysis technology has considerable potential for improving the efficiency and performance of breast cancer diagnosis.

Glycated hemoglobin (HbA1c) has been increasingly accepted as the gold standard for diabetes monitoring. In this study, Raman spectroscopy was tentatively employed for human hemoglobin (Hb) biochemical analysis aimed at developing a simple blood test for diabetes monitoring. Raman spectroscopy measurements were performed on hemoglobin samples of patients (n = 39) with confirmed diabetes and healthy volunteers (n = 37). The tentative assignments of the measured Raman bands were performed to compare the difference between these two groups. Meanwhile, principal component analysis (PCA) combined with linear discriminant analysis (LDA) were employed to develop effective diagnostic algorithms for classification between normal controls and patients with diabetes. As a result, the spectral features of these two groups demonstrated two distinct clusters with a sensitivity and specificity of 92.3% and 73%, respectively. Then the effectiveness of the diagnostic algorithm based on PCA-LDA technique was confirmed by receiver operating characteristic (ROC) curve. The area under the ROC curve was 0.92, indicating a good diagnostic result. In summary, our preliminary results demonstrate that proposing Raman spectroscopy can provide a significant potential for the noninvasive detection of diabetes.