Biospectroscopy for Plant and Crop Science

Multiblock NIR and MIR spectralprint through AComDim to evaluate the effects of growing site, harvest season, and clone on yerba mate leaves composition

The composition of yerba mate implies significant potential in the food, pharmaceutical, and cosmetic industries, which requires standardization of the raw material. This study explores the simultaneous influence of growing sites, harvest seasons, and clones on the spectralprint of leaves through near-infrared (NIR) and mid-infrared (MIR) spectroscopy coupled with ANOVA Common Dimensions (AComDim) multivariate analysis. MIR spectroscopy identifies only the main effects of growing site and harvesting season, and the interaction between these factors. The NIR spectralprint identifies all main effects and interactions. Growing site and harvesting season individually account for approximately 7 % of the variance in the chemical composition of yerba mate, with their interaction contributing with 5.7 %. Clonal variation significantly affects the spectral profile with approximately 4 % variance, which allowed the identification of clones with the highest chemical divergence. The study demonstrates that biospectroscopics and chemometrics can enhance yerba mate quality through clonal selection and optimized agricultural practices.

Polywave LEDs increase the degree of conversion of composite resins, but not adhesive systems: a systematic review and meta-analysis of in vitro studies

This study aimed to systematically review the literature for laboratory studies that evaluated the influence of mono- and polywave light-emitting diode (LED) devices on the degree of conversion (DC) of composite resins and adhesive systems. A search of three electronic databases (PubMed, Web of Science, Scopus) and using Google Scholar was conducted through June 2024 to identify eligible studies that compared monowave and polywave LED devices on the DC of composite resins and adhesive systems. Studies that evaluated DC using indirect methods, material other than composite resin and adhesive system, and missing DC values as mean and standard deviation were excluded. Meta-analysis was performed at a significance level of ≤ 0.05 comparing DC values (mean and standard deviation) produced by LEDs. Heterogeneity was quantified using I2 values. 79 potentially relevant studies were identified from online databases, 27 were selected for full-text assessment, and 22 were included in this systematic review. Eighteen of the included studies had a high risk of bias and four had a moderate risk of bias. Polywave LEDs significantly improved the DC of composite resins (p ≤ 0.5) and did not affect on adhesive systems (p = 0.18). Despite the high risk of bias and the heterogeneity of the included studies, the available evidence may support the conclusion that polywave LED devices improve the DC of composite resins. Monowave or polywave LED devices did not affect the DC of adhesive systems.

Biospectroscopic fingerprinting phytotoxicity towards environmental monitoring for food security and contaminated site remediation

Human activities have resulted in severe environmental pollution since the industrial revolution. Phytotoxicity-based environmental monitoring is well known due to its sedentary nature, abundance, and sensitivity to environmental changes, which are essential preconditions to avoiding potential environmental and ecological risks. However, conventional morphological and physiological methods for phytotoxicity assessment mainly focus on descriptive determination rather than mechanism analysis and face challenges of labour and time-consumption, lack of standardized protocol and difficulties in data interpretation. Molecular-based tests could reveal the toxicity mechanisms but fail in real-time and in-situ monitoring because of their endpoint manner and destructive operation in collecting cellular components. Herein, we systematically propose and lay out a biospectroscopic tool (e.g., infrared and Raman spectroscopy) coupled with multivariate data analysis as a relatively non-destructive and high-throughput approach to quantitatively measure phytotoxicity levels and qualitatively profile phytotoxicity mechanisms by classifying spectral fingerprints of biomolecules in plant tissues in response to environmental stresses. With established databases and multivariate analysis, this biospectroscopic fingerprinting approach allows ultrafast, in situ and on-site diagnosis of phytotoxicity. Overall, the proposed protocol and validation of biospectroscopic fingerprinting phytotoxicity can distinguish the representative biomarkers and interrogate the relevant mechanisms to quantify the stresses of interest, e.g., environmental pollutants. This state-of-the-art concept and design broaden the knowledge of phytotoxicity assessment, advance novel implementations of phytotoxicity assay, and offer vast potential for long-term field phytotoxicity monitoring trials in situ.

Predicting cow milk quality traits from routinely available milk spectra using statistical machine learning methods

Numerous statistical machine learning methods suitable for application to highly correlated features, as those that exist for spectral data, could potentially improve prediction performance over the commonly used partial least squares approach. Milk samples from 622 individual cows with known detailed protein composition and technological trait data accompanied by mid-infrared spectra were available to assess the predictive ability of different regression and classification algorithms. The regression-based approaches were partial least squares regression (PLSR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), elastic net, principal component regression, projection pursuit regression, spike and slab regression, random forests, boosting decision trees, neural networks (NN), and a post-hoc approach of model averaging (MA). Several classification methods (i.e., partial least squares discriminant analysis (PLSDA), random forests, boosting decision trees, and support vector machines (SVM)) were also used after stratifying the traits of interest into categories. In the regression analyses, MA was the best prediction method for 6 of the 14 traits investigated [curd firmness at 60 min, α_S1-casein (CN), α_S2-CN, κ-CN, α-lactalbumin, and β-lactoglobulin B], whereas NN and RR were the best algorithms for 3 traits each (rennet coagulation time, curd-firming time, and heat stability, and curd firmness at 30 min, β-CN, and β-lactoglobulin A, respectively), PLSR was best for pH, and LASSO was best for CN micelle size. When traits were divided into 2 classes, SVM had the greatest accuracy for the majority of the traits investigated. Although the well-established PLSR-based method performed competitively, the application of statistical machine learning methods for regression analyses reduced the root mean square error compared with PLSR from between 0.18% (κ-CN) to 3.67% (heat stability). The use of modern statistical machine learning methods for trait prediction from mid-infrared spectroscopy may improve the prediction accuracy for some traits.

Multi-stage screening to predict the specific anticancer activity of Ni(II) mixed-ligand complex on gastric cancer cells; biological activity, FTIR spectrum, DNA binding behavior and simulation studies

The anticancer activity of a transition metal complex with [Ni(L¹)₂L²]H₂O (where L¹ and L² were acetylacetonato (acac) and 2-aminopyridine (2-ampy), respectively) was evaluated in MKN45 cell line. Methyl thiazolyl tetrazolium (MTT) assay was performed to assess the antitumor capacity of the Ni(II) complex against gastric cancer cell line MKN45. The complexexhibited high in vitro antitumor activity against MKN45 cells with IC₅₀values of 1.99 μM in 48 hrs. The alterations in the structure of cellular biomolecules (proteins, lipids, carbohydrates, and especially DNA) by the Ni(II) complex were confirmed by bio spectroscopic studies. Fourier Transformed Infrared (FTIR) spectroscopy analysis revealed significant differences between untreated and treated MKN45 cell line in the region of glycogen, nucleic acid, amide I and amide II bands (1000, 1100, ~1650, and ~1577 cm^-1). The absorption bands 1150 cm^-1 and 1020-1025 cm^-1 can be assigned to the CO bond of glycogen and other carbohydrates and are significantly overlapped by DNA. The interaction of calf thymus (CT) DNA with Ni(II) complex was explored using absorption spectral method. The UV-visible studies demonstrated that this complex was able to bind with DNA via groove, non-covalent, and electrostatic interactions, and binding constant (K_b) was found to be 3 * 10⁴. Docking simulation and Non Covalent Interaction (NCI) topological analysis were conducted to provide insights into the nature of DNA/complex interactions. The binding affinity and binding stability of complex was validated by 400-ns MD simulations.

Development of a methodology to analyze leaves from Prunus dulcis varieties using near infrared spectroscopy

Near-infrared spectroscopy (NIRS) can be a faster and more economical alternative to traditional methods for screening varietal mixtures of nursery plants during the propagation process to ensure varietal purity and to avoid errors in the dispatch batches. The global objective of this work was to develop and optimize a NIR spectral collection method for construction of robust multivariate discrimination models. Three different varieties of Prunus dulcis (Avijor, Guara, and Pentacebas) of agricultural interest were used for this study. Sources of variation were investigated, including the position of the leaves on the trees, differences among trees of the same variety, and differences at the varietal level. Three types of processed samples were investigated. Fresh leaves, dried leaves, and dried leaves in powder form were included in each analysis. A study of spectral pre-treatment methods was also performed, and multivariate methods were applied to analyze the influence of different factors on classification. These included principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and ANOVA simultaneous component analysis (ASCA). The results indicated that variety was the most important factor for classification. The spectral pre-treatment that provided the best results was a combination of standard normal variate (SNV), Savitzky-Golay first derivative, and mean-centering methods. With regard to the type of processed sample, the highest percentages of correct classifications were obtained with fresh and dried powdered leaves at both the training set and test set validation levels. This study represents the first step towards the consolidation of NIRS as a method to identify Prunus dulcis varieties.