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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aceh is an important region for the production of high-quality Gayo arabica coffee in Indonesia. In this area, several coffee cherry processing methods are well implemented including the honey process (HP), wine process (WP), and natural process (NP). The most significant difference between the three coffee cherry processing methods is the fermentation process: HP is a process of pulped coffee bean fermentation, WP is coffee cherry fermentation, and NP is no fermentation. It is well known that the WP green coffee beans are better in quality and are sold at higher prices compared with the HP and NP green coffee beans. In this present study, we evaluated the utilization of fluorescence information to discriminate Gayo arabica green coffee beans from different cherry processing methods using portable fluorescence spectroscopy and chemometrics analysis. A total of 300 samples were used (n = 100 for HP, WP, and NP, respectively). Each sample consisted of three selected non-defective green coffee beans. Fluorescence spectral data from 348.5 nm to 866.5 nm were obtained by exciting the intact green coffee beans using a portable spectrometer equipped with four 365 nm LED lamps. The result showed that the fermented green coffee beans (HP and WP) were closely mapped and mostly clustered on the left side of PC1, with negative scores. The non-fermented (NP) green coffee beans were clustered mostly on the right of PC1 with positive scores. The results of the classification using partial least squares–discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and principal component analysis–linear discriminant analysis (PCA-LDA) are acceptable, with an accuracy of more than 80% reported. The highest accuracy of prediction of 96.67% was obtained by using the PCA-LDA model. Our recent results show the potential application of portable fluorescence spectroscopy using LED lamps to classify and authenticate the Gayo arabica green coffee beans according to their different cherry processing methods. This innovative method is more affordable and could be easy to implement (in terms of both affordability and practicability) in the coffee industry in Indonesia. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The rapid identification and recognition of COVID-19 have been challenging since its outbreak. Multiple methods were developed to realize fast monitoring early to prevent and control the pandemic. In addition, it is difficult and unrealistic to apply the actual virus to study and research because of the highly infectious and pathogenic SARS-CoV-2. In this study, the virus-like models were designed and produced to replace the original virus as bio-threats. Three-dimensional excitation-emission matrix fluorescence and Raman spectroscopy were employed for differentiation and recognition among the produced bio-threats and other viruses, proteins, and bacteria. Combined with PCA and LDA analysis, the identification of the models for SARS-CoV-2 was achieved, reaching a correction of 88.9% and 96.3% after cross-validation, respectively. This idea might provide a possible pattern for detecting and controlling SARS-CoV-2 from the perspective of combining optics and algorithms, which could be applied in the early-warning system against COVID-19 or other bio-threats in the future. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study we aimed to investigate the effect of heat treatment on the spectral pattern of honey using near infrared spectroscopy (NIRS). For the research, sunflower, bastard indigo, and acacia honeys were collected from entrusted beekeepers. The honeys were not subject to any treatment before. Samples were treated at 40 °C, 60 °C, 80 °C, and 100 °C for 60, 120, 180, and 240 min. This resulted in 17 levels, including the untreated control samples. The 5-hydroxymethylfurfural (HMF) content of the honeys was determined using the Winkler method. NIRS spectra were recorded using a handheld instrument. Data analysis was performed using ANOVA for the HMF content and multivariate analysis for the NIRS data. For the latter, PCA, PCA-LDA, and PLSR models were built (using the 1300–1600 nm spectral range) and the wavelengths presenting the greatest change induced by the perturbations of temperature and time intervals were collected systematically, based on the difference spectra and the weights of the models. The most contributing wavelengths were used to visualize the spectral pattern changes on the aquagrams in the specific water matrix coordinates. Our results showed that the heat treatment highly contributed to the formation of free or less bonded water, however, the changes in the spectral pattern highly depended on the crystallization phase and the honey type. [ABSTRACT FROM AUTHOR]

Coffee is both a vastly consumed beverage and a chemically complex matrix. For a long time, an arduous chemical analysis was necessary to resolve coffee authentication issues. Despite their demonstrated efficacy, such techniques tend to rely on reference methods or resort to elaborate extraction steps. Near infrared spectroscopy (NIRS) and the aquaphotomics approach, on the other hand, reportedly offer a rapid, reliable, and holistic compositional overview of varying analytes but with little focus on low concentration mixtures of Robusta-to-Arabica coffee. Our study aimed for a comparative assessment of ground coffee adulteration using NIRS and liquid coffee adulteration using the aquaphotomics approach. The aim was to demonstrate the potential of monitoring ground and liquid coffee quality as they are commercially the most available coffee forms. Chemometrics spectra analysis proved capable of distinguishing between the studied samples and efficiently estimating the added Robusta concentrations. An accuracy of 100% was obtained for the varietal discrimination of pure Arabica and Robusta, both in ground and liquid form. Robusta-to-Arabica ratio was predicted with R2CV values of 0.99 and 0.9 in ground and liquid form respectively. Aquagrams results accentuated the peculiarities of the two coffee varieties and their respective blends by designating different water conformations depending on the coffee variety and assigning a particular water absorption spectral pattern (WASP) depending on the blending ratio. Marked spectral features attributed to high hydrogen bonded water characterized Arabica-rich coffee, while those with the higher Robusta content showed an abundance of free water structures. Collectively, the obtained results ascertain the adequacy of NIRS and aquaphotomics as promising alternative tools for the authentication of liquid coffee that can correlate the water-related fingerprint to the Robusta-to-Arabica ratio. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the clinical potential of Raman spectroscopy (RS) in detecting oral squamous cell carcinoma (OSCC) in tumor and healthy tissues in surgical resection specimens during surgery. Raman experiments were performed on cryopreserved specimens from patients with OSCC. Univariate and multivariate analysis was performed based on the fingerprint region (700–1800 cm − 1 ) of the Raman spectra. One hundred thirty-one ex-vivo Raman experiments were performed on 131 surgical resection specimens obtained from 67 patients. The principal component analysis (PCA) and partial least square (PLS) methods with linear discriminant analysis (LDA) were applied on an independent validation dataset. Both models were able to differentiate between the tissue types, but PLS–LDA showed 100% accuracy, sensitivity, and specificity. In this study, Raman measurements of fresh resection tissue specimens demonstrated that OSCC had significantly higher nucleic acid, protein, and several amino acid contents than adjacent healthy tissues. The specific spectral information obtained in this study can be used to develop an in vivo Raman spectroscopic method for the tumor-free resection boundary during surgery. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Raman spectroscopy (RS) is widely used as a non-invasive technique in screening for the diagnosis of oral cancer. The potential of this optical technique for several biomedical applications has been proved. This work studies the efficacy of RS in detecting oral cancer using sub-site-wise differentiation. A total of 80 samples (44 tumor and 36 normal) were cryopreserved from three different sub-sites: The tongue, the buccal mucosa, and the gingiva of the oral mucosa during surgery. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were used with principal component analysis (PCA) to classify the samples and the classifications were validated by leave-one-out-cross-validation (LOOCV) and k-fold cross-validation methods. The normal and tumor tissues were differentiated under the PCA-LDA model with an accuracy of 81.25% (sensitivity: 77.27%, specificity: 86.11%). The PCA-QDA classifier model differentiated these tissues with an accuracy of 87.5% (sensitivity: 90.90%, specificity: 83.33%). The PCA-QDA classifier model outperformed the PCA-LDA-based classifier. The model studies revealed that protein, amino acid, and beta-carotene variations are the main biomolecular difference markers for detecting oral cancer. [ABSTRACT FROM AUTHOR]