Deep Learning-Based Quantitative Assessment of Melamine and Cyanuric Acid in Pet Food Using Fourier Transform Infrared Spectroscopy.

Melamine and its derivative, cyanuric acid, are occasionally added to pet meals because of their nitrogen-rich qualities, leading to the development of several health-related issues. A nondestructive sensing technique that offers effective detection must be developed to address this problem. In conjunction with machine learning and deep learning technique, Fourier transform infrared (FT-IR) spectroscopy was employed in this investigation for the nondestructive quantitative measurement of eight different concentrations of melamine and cyanuric acid added to pet food. The effectiveness of the one-dimensional convolutional neural network (1D CNN) technique was compared with that of partial least squares regression (PLSR), principal component regression (PCR), and a net analyte signal (NAS)-based methodology, called hybrid linear analysis (HLA/GO). The 1D CNN model developed for the FT-IR spectra attained correlation coefficients of 0.995 and 0.994 and root mean square error of prediction values of 0.090% and 0.110% for the prediction datasets on the melamine- and cyanuric acid-contaminated pet food samples, respectively, which were superior to those of the PLSR and PCR models. Therefore, when FT-IR spectroscopy is employed in conjunction with a 1D CNN model, it serves as a potentially rapid and nondestructive method for identifying toxic chemicals added to pet food.

Therapeutic Antibodies in Medicine.

Antibody engineering has developed into a wide-reaching field, impacting a multitude of industries, most notably healthcare and diagnostics. The seminal work on developing the first monoclonal antibody four decades ago has witnessed exponential growth in the last 10-15 years, where regulators have approved monoclonal antibodies as therapeutics and for several diagnostic applications, including the remarkable attention it garnered during the pandemic. In recent years, antibodies have become the fastest-growing class of biological drugs approved for the treatment of a wide range of diseases, from cancer to autoimmune conditions. This review discusses the field of therapeutic antibodies as it stands today. It summarizes and outlines the clinical relevance and application of therapeutic antibodies in treating a landscape of diseases in different disciplines of medicine. It discusses the nomenclature, various approaches to antibody therapies, and the evolution of antibody therapeutics. It also discusses the risk profile and adverse immune reactions associated with the antibodies and sheds light on future applications and perspectives in antibody drug discovery.

A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions.

The human immune system is very complex. Understanding it traditionally required specialized knowledge and expertise along with years of study. However, in recent times, the introduction of technologies such as AIoMT (Artificial Intelligence of Medical Things), genetic intelligence algorithms, smart immunological methodologies, etc., has made this process easier. These technologies can observe relations and patterns that humans do and recognize patterns that are unobservable by humans. Furthermore, these technologies have also enabled us to understand better the different types of cells in the immune system, their structures, their importance, and their impact on our immunity, particularly in the case of debilitating diseases such as cancer. The undertaken study explores the AI methodologies currently in the field of immunology. The initial part of this study explains the integration of AI in healthcare and how it has changed the face of the medical industry. It also details the current applications of AI in the different healthcare domains and the key challenges faced when trying to integrate AI with healthcare, along with the recent developments and contributions in this field by other researchers. The core part of this study is focused on exploring the most common classifications of health diseases, immunology, and its key subdomains. The later part of the study presents a statistical analysis of the contributions in AI in the different domains of immunology and an in-depth review of the machine learning and deep learning methodologies and algorithms that can and have been applied in the field of immunology. We have also analyzed a list of machine learning and deep learning datasets about the different subdomains of immunology. Finally, in the end, the presented study discusses the future research directions in the field of AI in immunology and provides some possible solutions for the same.

Hyperspectral Shortwave Infrared Image Analysis for Detection of Adulterants in Almond Powder with One-Class Classification Method.

The widely used techniques for analyzing the quality of powdered food products focus on targeted detection with a low-throughput screening of samples. Owing to potentially significant health threats and large-scale adulterations, food regulatory agencies and industries require rapid and non-destructive analytical techniques for the detection of unexpected compounds present in products. Accordingly, shortwave-infrared hyperspectral imaging (SWIR-HSI) for high throughput authenticity analysis of almond powder was investigated in this study. Two different varieties of almond powder, adulterated with apricot and peanut powder at different concentrations, were imaged using the SWIR-HSI system. A one-class classifier technique, known as data-driven soft independent modeling of class analogy (DD-SIMCA), was used on collected data sets of pure and adulterated samples. A partial least square regression (PLSR) model was further developed to predict adulterant concentrations in almond powder. Classification results from DD-SIMCA yielded 100% sensitivity and 89-100% specificity for different validation sets of adulterated samples. The results obtained from the PLSR analysis yielded a high determination coefficient (R2) and low error values (<1%) for each variety of almond powder adulterated with apricot; however, a relatively higher error rates of 2.5% and 4.4% for the two varieties of almond powder adulterated with peanut powder, which indicates the performance of quantitative analysis model could vary with sample condition, such as variety, originality, etc. PLSR-based concentration mapped images visually characterized the adulterant (apricot) concentration in the almond powder. These results demonstrate that the SWIR-HSI technique combined with the one-class classifier DD-SIMCA can be used effectively for a high-throughput quality screening of almond powder regarding potential adulteration.

Treatment of diffuse large B-cell lymphoma with secondary central nervous system involvement: encouraging efficacy using CNS-penetrating R-IDARAM chemotherapy.

Diffuse large B-cell lymphoma with secondary involvement of the central nervous system (SCNS-DLBCL) is a rare condition carrying a poor prognosis. No optimal therapeutic regimen has been identified. We retrospectively analysed 23 patients with SCNS-DLBCL treated with R-IDARAM (rituximab 375 mg/m(2) IV day 1; methotrexate 12·5 mg by intrathecal injection day 1; idarubicin 10 mg/m(2) /day IV days 1 and 2; dexamethasone 100 mg/day IV infusion over 12 h days 1-3; cytosine arabinoside 1000 mg/m(2) /day IV over 1 h days 1 and 2; and methotrexate 2000 mg/m(2) IV over 2 h day 3. Ten out of 23 (44%) patients had CNS involvement at initial presentation ('new disease'), 10/23 (44%) had relapsed disease and 3/23 (13%) had primary refractory disease. 14/23 (61%) of patients responded - 6 (26%) complete response, 8 (35%) partial response. Grade 3-4 haematological toxicity was seen in all cycles, with no grade 3-4 or long-term neurological toxicity. Median follow-up for surviving patients was 49 months. At 2 years, estimated progression-free survival (PFS) was 39% and overall survival (OS) was 52%. Encouraging outcomes were reported in patients with new disease, with 5-year estimated PFS of 50% and OS 75%. R-IDARAM is a well-tolerated regimen with encouraging efficacy in patients with SCNS-DLBCL, although patients with relapsed or refractory disease continue to fare poorly.

Spontaneously occurring fundus findings observed using confocal scanning laser ophthalmoscopy in wild type Sprague Dawley rats.

PURPOSE: Non-invasive in vivo imaging is an increasingly used component of pre-clinical research. However, to reliably interpret data, it may be necessary to identify and document pre-existent findings prior to initiating long-term or intensive protocols, particularly where toxicity or efficacy is under investigation. Here we report here spontaneously occurring findings from the Sprague Dawley (SD) rat eye using multi-modal confocal scanning laser ophthalmoscopy (cSLO). METHODS: As part of ongoing studies, with the goal of excluding animals with abnormalities from further investigation, a total of 165 wild type SD rats (312 eyes) were assessed using cSLO imaging at baseline prior to initiating experiments to detect, describe, and determine the prevalence of spontaneous fundus findings. RESULTS: Using fundus autofluorescence (FAF) as the primary screening modality, over 30% of analyzed eyes possessed some fundus finding that differed from the normal composite reference image. Unexpectedly, 100% of eyes demonstrated a diffuse hyperfluorescent region in the posterior pole that was ultimately considered normal, and formed part of the reference. Evaluated by three independent reviewers, five groups of FAF abnormalities were defined, based primarily on shape and size of the lesion. Of these, the most extensive lesions were further analyzed using infrared reflectance (IR) and red free (RF) imaging. White light and autofluorescent microscopy of excised tissue confirmed that the extensive lesions were derived from abnormalities in both the isolated retina and posterior eyecups. CONCLUSIONS: Given the newly described hyperfluorescent glow that appears in all eyes, and the high basal rate of spontaneous lesions in the outbred SD rat, we suggest that investigators be aware of the variants of normal, and that baseline in vivo screening be considered prior to initiating intensive or expensive investigation.

Dried fish dataset for Indian seafood: A machine learning application.

Dryingfish is a simple and economical way to process the catch. It creates a profitable business for coastal communities by providing a market for their catches, even during periods of abundance. It's a traditional method to preserve fish, especially valuable in regions where fresh fish isn't readily available or affordable throughout the year. This dataset provides a rich resource of 8290 images specifically designed for machine learning applications. It focuses on the five most popular types of dried seafood in India: prawns (shrimp), small anchovies (tingali), golden anchovies (mandeli), mackerel (bangada), and Bombay duck (bombil). To ensure high-quality data for machine learning applications for Identification and classification of different dried fish varieties, the dataset features a diverse set of images in singles and in bulk for each category. The dataset utilizes standardized lighting, background, and object pose for optimal machine learning performance. This rich dataset empowers researchers and data scientists to leverage machine learning for various applications in the Indian dried fish industry.Overall, the Dried Fish Dataset for Indian Seafood aims to leverage machine learning to improve the standardization, quality control, safety, and efficiency of the Indian dried fish industry.

A catalogue of Indian Gelechiidae Stainton, 1854 (Lepidoptera: Gelechioidea).

We present an annotated catalogue of the Lepidoptera family Gelechiidae of India, comprising 351 species in 80 genera, encompassing seven subfamilies. The Indian fauna represents 7.47% of global gelechiid species diversity (i.e., 4,700 species in 500 genera). Among the seven subfamilies, Dichomeridinae is the best represented (122 species in four genera), followed by Gelechiinae (76 species in 38 genera), Anacampsinae (70 species in 16 genera), Thiotrichinae (49 species in five genera), Anomologinae (26 species in nine genera), Apatetrinae (seven species in seven genera), and Physoptilinae(single species). Information on type locality, type repository, synonyms, geographical distribution, hosts, natural enemies, and references to illustrations are provided. We also reviewed the history of descriptive work on the Gelechiidae of India, and resolved ambiguities regarding the current status of some species.

Trace level detection of melamine and cyanuric acid extracted from pet liquid food (milk) using a SERS Au nanogap substrate.

This study reported an application of Au nanogap substrates for surface-enhanced Raman scattering (SERS) measurements to quantitatively analyze melamine and its derivative products at trace levels in pet liquid food (milk) combined with a waveband selection approach, namely variable importance in projection (VIP). Six different concentrations of melamine, cyanuric acid, and melamine combined with cyanuric acid were created, and SERS spectra were acquired from 550 to 1620cm-1. Detection was possible up to 200 pM for melamine-contaminated samples, and 400 pM concentration detection for other two groups. The VIP-PLSR models obtained correlation coefficient (R2) values of 0.997, 0.985, and 0.981, with root mean square error of prediction (RMSEP) values of 18.492 pM, 19.777 pM, and 15.124 pM for prediction datasets. Additionally, partial least square discriminant analysis (PLS-DA) was used to classify both pure and different concentrations of spiked samples. The results showed that the maximum classification accuracy for melamine was 100%, for cyanuric acid it was 96%, and for melamine coupled with cyanuric acid it was 95%. The results obtained clearly demonstrated that the Au nanogap substrate offers low-concentration, rapid, and efficient detection of hazardous additive chemicals in pet consuming liquid food.

Ecological restoration at pilot-scale employing site-specific rationales for small-patch degraded mangroves in Indian Sundarbans.

To date, degraded mangrove ecosystem restoration accomplished worldwide primarily aligns towards rehabilitation with monotypic plantations, while ecological restoration principles are rarely followed in these interventions. However, researchers admit that most of these initiatives' success rate is not appreciable often. An integrative framework of ecological restoration for degraded mangroves where site-specific observations could be scientifically rationalized, with co-located reference pristine mangroves as the target ecosystem to achieve is currently distinctively lacking. Through this experimental scale study, we studied the suitability of site-specific strategies to ecologically restore degraded mangrove patches vis-à-vis the conventional mono-species plantations in a highly vulnerable mangrove ecosystem in Indian Sundarbans. This comprehensive restoration framework was trialed in small discrete degraded mangrove patches spanning ~ 65 ha. Site-specific key restoration components applied are statistically validated through RDA analyses and Bayesian t-tests. 25 quantifiable metrics evaluate the restoration success of a ~ 3 ha degraded mangrove patch with Ridgeline distribution, Kolmogorov-Smirnov (K-S) tests, and Mahalanobis Distance (D2) measure to prove the site's near-equivalence to pristine reference in multiple ecosystem attributes. This restoration intervention irrevocably establishes the greater potential of this framework in the recovery of ecosystem functions and self-sustenance compared to that of predominant monoculture practices for vulnerable mangroves.

An annotated catalogue of Indian Lasiocampidae (Lasiocampoidea, Lepidoptera).

We catalogue 100 species (including nominotypic subspecies) and five subspecies in 35 genera of Lasiocampinae, Lasiocampidae from India. This represents 5% of the 1,952 species of Global Lasiocampidae. Information on types (locality and depository), synonymy, diversity and distribution in different biogeographic zones of India alongwith global records, contribution of various authors on Indian Lasiocampidae is provided. Apart from seven doubtful records, clarifications are given as remarks wherever needed. Additionally, Streblote alpherakyi (Christoph, 1885) and Odontocraspis collieri Zolotuhin & Witt, 2000a are reported for the first time from India.

An updated Catalogue of Cossoidea (Lepidoptera) from India.

We catalogue 165 species in 81 genera representing six families of Cossoidea present in India. Sesiidae is the most dominant family in India, consisting 89 species in 38 genera, followed by Cossidae with 47 species in 28 genera, Brachodidae with 13 species in six genera, Metarbelidae with 12 species in seven genera, Ratardidae with three species in single genus and the Dudgeoneidae is known by single species. A comprehensive data on the species diversity of all the families of Indian Cossoidea, with information on type species, type locality, synonyms, clarifications, and distribution in different parts of India and Globe, are provided.

Taxonomic review of the Miltochrista hollowai and M. curvifascia species-groups with descriptions of two new species from India (Erebidae: Arctiinae: Lithosiini).

The Miltochrista hollowai (Kirti & Gill, 2009) and M. curvifascia (Hampson, 1891) species-groups are reviewed, and two new species belonging to the M. hollowai species-group i.e., Miltochrista madathumala sp. nov. and M. kumarkaustubhi sp. nov. are described from South India. The female of M. paraarcuata is illustrated and diagnosed for the first time. Illustrations of adults and genitalia as well as diagnoses are provided for all the species considered.

To evaluate serum cortisol levels in patients with alcohol withdrawal delirium v/s patients with delirium due to any other disorder.

Background: Delirium is an acute confusional state characterized by changes in the mental status, level of consciousness, impaired cognition, and inattention. It can develop within hours or days. Cortisol release from the hypothalamic-pituitary-adrenal axis (HPA) is vital for the host survival in stress. Biomarkers are used as an indicator of pathogenic processes or to assess the responses to a therapeutic intervention. To improve delirium recognition and care, investigators have identified possible biomarkers that may help in diagnosing individuals with delirium, assessing the severity of delirium. Cortisol has been suggested as biomarker for the diagnosis of delirium. Aims and Objectives: To evaluate and compare levels of serum cortisol in patients with alcohol withdrawal delirium with delirium due to other disorders. Materials and Methods: It was a cross-sectional prospective observational study. A total of 30 patients in Group A and 32 in Group B were included. The participants were evaluated based on delirium rating scale (DRS). Results: It was seen that in alcohol withdrawal delirium group, there was significant positive correlation between DRS score and serum cortisol level, i.e., with increase in DRS score, there was increase in serum cortisol levels and vice versa. Conclusion: Serum cortisol levels are associated and directly correlate with the occurrence and severity of delirium. Further studies are needed to elucidate the implications of this association for diagnosis and treatment.

Au nanogap SERS substrate for the carbaryl pesticide determination in juice and milk using chemomterics.

Conventional spectroscopic methods like IR, and Raman are not very effective at detecting low levels of pesticides or harmful chemicals in food matrices. A quick, highly accurate approach that can identify pesticides present in different food products at lower levels must be developed in order to address this problem and ensure food safety. In this study, a highly sensitive and uniform wafer-scale Au nanogap surface-enhanced Raman spectroscopy (SERS) substrate was used for the quantitative analysis of carbaryl pesticide levels in standard solution, mango juice, and milk samples using chemometrics. Carbaryl was detected up to 3 ppb concentration levels for all three group of samples. However, due to the higher sensitivity, uniformity, and enhancement factors of the SERS substrate used in this investigation, the limit of detection (LOD) values for the standard solution, mango juice, and milk were 0.37 ppb, 0.57 ppb, and 0.15 ppb at 1380 cm-1, 1380 cm-1, and 1364 cm-1 wavenumber ranges. In order to predict different carbaryl concentrations (1, 2, 3, 4, and 5 ppb), the variable importance in projection (VIP) method combined with partial least squares regression (PLSR) and attained the coefficient of determination (R2) values of 0.994, 0.989, and 0.978 along with minimum root mean square error (RMSE) values of 0.112, 0.190, and 0.278 ppb for the prediction datasets. Furthermore, PLS-DA was able to distinguish between pure and adulterated samples with the highest classification accuracy of 100 % for a standard solution, and mango juice and 94.4 % for milk samples. Considering this, we can conclude that the SERS Au Nanogap substrate can rapidly and effectively detect carbaryl pesticides quantitatively and qualitatively in mango juice and milk.

Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation.

In accordance with the inability of various hair artefacts subjected to dermoscopic medical images, undergoing illumination challenges that include chest-Xray featuring conditions of imaging acquisi-tion situations built with clinical segmentation. The study proposed a novel deep-convolutional neural network (CNN)-integrated methodology for applying medical image segmentation upon chest-Xray and dermoscopic clinical images. The study develops a novel technique of segmenting medical images merged with CNNs with an architectural comparison that incorporates neural networks of U-net and fully convolutional networks (FCN) schemas with loss functions associated with Jaccard distance and Binary-cross entropy under optimised stochastic gradient descent + Nesterov practices. Digital image over clinical approach significantly built the diagnosis and determination of the best treatment for a patient's condition. Even though medical digital images are subjected to varied components clarified with the effect of noise, quality, disturbance, and precision depending on the enhanced version of images segmented with the optimised process. Ultimately, the threshold technique has been employed for the output reached under the pre- and post-processing stages to contrast the image technically being developed. The data source applied is well-known in PH2 Database for Melanoma lesion segmentation and chest X-ray images since it has variations in hair artefacts and illumination. Experiment outcomes outperform other U-net and FCN architectures of CNNs. The predictions produced from the model on test images were post-processed using the threshold technique to remove the blurry boundaries around the predicted lesions. Experimental results proved that the present model has better efficiency than the existing one, such as U-net and FCN, based on the image segmented in terms of sensitivity = 0.9913, accuracy = 0.9883, and dice coefficient = 0.0246.

A catalogue of Indian Pyraloidea (Lepidoptera).

We catalogue 1,695 Indian Pyraloidea species in 509 genera. Of these, Pyralidae comprises 518 species in 182 genera, which represents 8.35% of the global Pyralidae diversity of 6,197 species. Crambidae are represented by 1,177 species in 327 genera, accounting for 11.29% of the global Crambidae diversity of 10,418 species. Botys medullalis Snellen, distributed in Indonesia, is reinstated to species status as Sciorista medullalis (Snellen), stat. rev., comb. nov. Sylepta [sic] picalis Hampson, 1903, syn. nov. is synonymised with Syllepte picalis Hampson, 1899. A replacement name Archernis polynesiae N. Singh & Mally, nom. nov. is proposed for Archernis fulvalis Hampson, 1913a, a junior homonym of Archernis fulvalis Hampson, 1899e. We review the chronology and quantity of species descriptions of Indian Pyraloidea by various authors. Summaries of all subfamilies of Pyralidae and Crambidae present in India provide information on adult and larval morphology, food plant associations, and diversity and distribution in major biogeographic zones of India.

Evolving Patterns of Hyperfluorescent Fundus Autofluorescence Accompany Retinal Atrophy in the Rat and Mimic Atrophic Age-Related Macular Degeneration.

PURPOSE: Complex two-dimensional (2D) patterns of hyperfluorescent short-wave fundus autofluorescence (FAF) at the border of geographic atrophy (GA) can predict its expansion in patients with late non-exudative "dry" AMD. However, preclinical models do not phenocopy this important feature of disease. We sought to describe the spatiotemporal changes in hyperfluorescent FAF patterns that occur following acute oxidative stress, potentially in association with GA expansion. METHODS: Sprague Dawley rats (n = 54) received systemic sodium iodate (25-45 mg/kg, n = 90 eyes) or saline (n = 18 eyes) and underwent serial full fundus imaging by confocal scanning laser ophthalmoscopy, including blue FAF and delayed near-infrared analysis. Composite images of the fundus were assembled, and the 2D patterns were described qualitatively and quantitatively. A subset of eyes underwent tissue analysis, and four underwent optical coherence tomography (OCT) imaging. RESULTS: Reproducibly changing, complex patterns of hyperfluorescent FAF emerge at the borders of toxin-induced damage; however, in the absence of GA expansion, they percolate inward within the region of retinal pigment epithelium loss, evolving, maturing, and senescing in situ over time. Unexpectedly, the late FAF patterns most closely resemble the diffuse tricking form of clinical disease. A five-stage classification system is presented. CONCLUSIONS: Longitudinal, full-fundus imaging of outer retinal atrophy in the rat eye identifies evolving, complex patterns of hyperfluorescent FAF that phenocopy aspects of disease. TRANSLATIONAL RELEVANCE: This work provides a novel tool to assess hyperfluorescent FAF in association with progressive retinal atrophy, a therapeutic target in late AMD.

Non-destructive measurement of total phenolic compounds in Arabidopsis under various stress conditions.

Quantifying the phenolic compounds in plants is essential for maintaining the beneficial effects of plants on human health. Existing measurement methods are destructive and/or time consuming. To overcome these issues, research was conducted to develop a non-destructive and rapid measurement of phenolic compounds using hyperspectral imaging (HSI) and machine learning. In this study, the Arabidopsis was used since it is a model plant. They were grown in controlled and various stress conditions (LED lights and drought). Images were captured using HSI in the range of 400-1,000 nm (VIS/NIR) and 900-2,500 nm (SWIR). Initially, the plant region was segmented, and the spectra were extracted from the segmented region. These spectra were synchronized with plants' total phenolic content reference value, which was obtained from high-performance liquid chromatography (HPLC). The partial least square regression (PLSR) model was applied for total phenolic compound prediction. The best prediction values were achieved with SWIR spectra in comparison with VIS/NIR. Hence, SWIR spectra were further used. Spectral dimensionality reduction was performed based on discrete cosine transform (DCT) coefficients and the prediction was performed. The results were better than that of obtained with original spectra. The proposed model performance yielded R 2-values of 0.97 and 0.96 for calibration and validation, respectively. The lowest standard errors of predictions (SEP) were 0.05 and 0.07 mg/g. The proposed model out-performed different state-of-the-art methods. These demonstrate the efficiency of the model in quantifying the total phenolic compounds that are present in plants and opens a way to develop a rapid measurement system.

Comparative Determination of Phenolic Compounds in Arabidopsis thaliana Leaf Powder under Distinct Stress Conditions Using Fourier-Transform Infrared (FT-IR) and Near-Infrared (FT-NIR) Spectroscopy.

The increasing interest in plant phenolic compounds in the past few years has become necessary because of their several important physicochemical properties. Thus, their identification through non-destructive methods has become crucial. This study carried out comparative non-destructive measurements of Arabidopsis thaliana leaf powder sample phenolic compounds using Fourier-transform infrared and near-infrared spectroscopic techniques under six distinct stress conditions. The prediction analysis of 600 leaf powder samples under different stress conditions (LED lights and drought) was performed using PLSR, PCR, and NAS-based HLA/GO regression analysis methods. The results obtained through FT-NIR spectroscopy yielded the highest correlation coefficient (Rp2) value of 0.999, with a minimum error (RMSEP) value of 0.003 mg/g, based on the PLSR model using the MSC preprocessing method, which was slightly better than the correlation coefficient (Rp2) value of 0.980 with an error (RMSEP) value of 0.055 mg/g for FT-IR spectroscopy. Additionally, beta coefficient plots present spectral differences and the identification of important spectral signatures sensitive to the phenolic compounds in the measured powdered samples. Thus, the obtained results demonstrated that FT-NIR spectroscopy combined with partial least squares regression (PLSR) and suitable preprocessing method has a solid potential for non-destructively predicting phenolic compounds in Arabidopsis thaliana leaf powder samples.