Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants.

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.

Inline Raman spectroscopy as process analytical technology for SARS-CoV-2 VLP production.

The present work focused on inline Raman spectroscopy monitoring of SARS-CoV-2 VLP production using two culture media by fitting chemometric models for biochemical parameters (viable cell density, cell viability, glucose, lactate, glutamine, glutamate, ammonium, and viral titer). For that purpose, linear, partial least square (PLS), and nonlinear approaches, artificial neural network (ANN), were used as correlation techniques to build the models for each variable. ANN approach resulted in better fitting for most parameters, except for viable cell density and glucose, whose PLS presented more suitable models. Both were statistically similar for ammonium. The mean absolute error of the best models, within the quantified value range for viable cell density (375,000-1,287,500 cell/mL), cell viability (29.76-100.00%), glucose (8.700-10.500 g/), lactate (0.019-0.400 g/L), glutamine (0.925-1.520 g/L), glutamate (0.552-1.610 g/L), viral titer (no virus quantified-7.505 log10 PFU/mL) and ammonium (0.0074-0.0478 g/L) were, respectively, 41,533 ± 45,273 cell/mL (PLS), 1.63 ± 1.54% (ANN), 0.058 ± 0.065 g/L (PLS), 0.007 ± 0.007 g/L (ANN), 0.007 ± 0.006 g/L (ANN), 0.006 ± 0.006 g/L (ANN), 0.211 ± 0.221 log10 PFU/mL (ANN), and 0.0026 ± 0.0026 g/L (PLS) or 0.0027 ± 0.0034 g/L (ANN). The correlation accuracy, errors, and best models obtained are in accord with studies, both online and offline approaches while using the same insect cell/baculovirus expression system or different cell host. Besides, the biochemical tracking throughout bioreactor runs using the models showed suitable profiles, even using two different culture media.

Chemometrics and analytical blank on the at-line monitoring of Zika-VLP production using near-infrared spectroscopy.

The Zika disease caused by the Zika virus was declared a Public Health Emergency by the World Health Union (WHO), with microcephaly as the most critical consequence. Aiming to reduce the spread of the virus, biopharmaceutical organizations invest in vaccine research and production, based on multiple platforms. A crescent vaccine production approach is based on virus-like particles (VLP), for not having genetic material in its composition, hypoallergenic and non-mutant character. For bioprocess, it is essential to have means of real-time monitoring, which can be assessed using process analysis techniques such as Near-infrared (NIR) spectroscopy, that can be combined with chemometric methods, like Partial-Least Squares (PLS) and Artificial Neural Networks (ANN) for prediction of biochemical variables. This work proposes a biochemical Zika VLP upstream production at-line monitoring model using NIR spectroscopy comparing sampling conditions (with or without cells), analytical blank (air, ultrapure water), and spectra pre-processing approaches. Seven experiments in a benchtop bioreactor using recombinant baculovirus/Sf9 insect cell platform in serum-free medium were performed to obtain biochemical and spectral data for chemometrics modeling (PLS and ANN), composed by a random data split (80 % calibration, 20 % validation) for cross-validation of the PLS models and 70 % training, 15 % testing, 15 % validation for ANN. The best models generated in the present work presented an average absolute error of 1.59 × 105 cell/mL for density of viable cells, 2.37 % for cell viability, 0.25 g/L for glucose, 0.007 g/L for lactate, 0.138 g/L for glutamine, 0.18 g/L for glutamate, 0,003 g/L for ammonium, and 0.014 g/L for potassium.

Prediction of melanin content of Fonsecaea pedrosoi using Fourier transform infrared spectroscopy (FTIR) and chemometrics.

Fungal melanin contributes to the survival and virulence of pathogenic fungi, such as Fonsecaea pedrosoi, which is responsible for causing chromoblastomycosis. The objective of this study was to employ Fourier transform infrared spectroscopy (FTIR) to predict the melanin content of F. pedrosoi. The melanin content, in percentage, was previously determined using gravimetry for twenty-six clinical isolates. Quintuplicate spectra of each isolate were obtained using attenuated total reflection (ATR) within the range of 4000 to 650 cm-1. To predict the melanin content, modeling was performed using partial least squares regression (PLS) in the region 1800 - 750 cm-1. Two models were tested: PLS and successive projections algorithms for interval selection in partial least squares (iSPA-PLS). The best modeling results were achieved using iSPA-PLS with one factor. The calibration set exhibited a determination coefficient (R2) of 0.9745 and a root mean square error of cross-validation (RMSECV) of 0.0977. In the prediction set, the R2 value was 0.9711, and the root mean square error of prediction (RMSEP) was 0.0999. Modeling with FTIR and multivariate calibration provides a valuable means of predicting fungal melanin content, which is simpler and more robust, thereby contributing to the advancement of this field of study.

Assessing Polymorphic Purity of Rifampicin in Double and Triple-Drug Fixed-Dose Combination Products.

First-line tuberculostatic agents, Rifampicin (RIF), Isoniazid (ISH), Ethambutol (ETB), and Pyrazinamide (PZA) are generally administered as a fixed-dose combination (FDC) for improving patient adherence. The major quality challenge of these FDC products is their variable bioavailability, where RIF and its solid state are key factors. In this work, the analysis of the impact of the polymorphism in the performance of RIF in RIF-ISH and PZA-RIF-ISH combined products was carried out by an overall approach that included the development and validation of two methodologies combining near-infrared (NIR) spectroscopy and partial least squares (PLS) to the further evaluation of commercial products. For NIR-PLS methods, training and validation sets were prepared with mixtures of Form I/Form II of RIF, and the appropriate amount of ISH (for double associations) or ISH-PZA (for triple associations). The corresponding matrix of the excipients was added to the mixture of APIs to simulate the environment of each FDC product. Four PLS factors, reduced spectral range, and the combination of standard normal variate and Savitzky-Golay 1st derivative (SNV-D') were selected as optimum data pre-treatment for both methods, yielding satisfactory recoveries during the analysis of validation sets (98.5±2.0%, and 98.7±1.8% for double- and triple-FDC products, respectively). The NIR-PLS model for RIF-ISH successfully estimated the polymorphic purity of Form II in double-FDC capsules (1.02 ± 0.02w/w). On the other hand, the NIR-PLS model for RIF-ISH-PZA detected a low purity of Form II in triple FDC tablets (0.800 ± 0.021w/w), these results were confirmed by X-ray powder diffraction. Nevertheless, the triple-FDC tablets showed good performance in the dissolution test (Q=99-102%), implying a Form II purity about of 80% is not low enough to affect the safety and efficacy of the product.

Detection of carbon nanotubes in bovine raw milk through Fourier transform Raman spectroscopy.

The potential use of carbon-based methodologies for drug delivery and reproductive biology in cows raises concerns about residues in milk and food safety. This study aimed to assess the potential of Fourier transform Raman spectroscopy and discriminant analysis using partial least squares (PLS-DA) to detect functionalized multiwalled carbon nanotubes (MWCNT) in bovine raw milk. Oxidized MWCNT were diluted in milk at different concentrations from 25.00 to 0.01 µg/mL. Raman spectroscopy measurements and PLS-DA were performed to identify low concentrations of MWCNT in milk samples. The PLS-DA model was characterized by the analysis of the variable importance in projection (VIP) scores. All the training samples were correctly classified by the model, resulting in no false-positive or false-negative classifications. For test samples, only one false-negative result was observed, for 0.01 µg/mL MWCNT dilution. The association between Raman spectroscopy and PLS-DA was able to identify MWCNT diluted in milk samples up to 0.1 µg/mL. The PLS-DA model was built and validated using a set of test samples and spectrally interpreted based on the highest VIP scores. This allowed the identification of the vibrational modes associated with the D and G bands of MWCNT, as well as the milk bands, which were the most important variables in this analysis.

Characterization of biomarkers in blood serum for cancer diagnosis in dogs using Raman spectroscopy.

Biomarkers of cancer in sera of domestic dogs were detected through Raman spectroscopy with 830 nm excitation. Raman spectra of sera from 61 dogs (31 healthy and 30 with cancer, resulting in 154 and 200 spectra, respectively) were submitted to principal component analysis (PCA) for feature extraction and partial least squares (PLS) regression for discrimination between Healthy and Cancer groups. In the PCA, the peaks at 1132, 1342, 1368, and 1453 cm-1 (albumin and phenylalanine) were higher for the Cancer group. The "redshift" of the peaks at 621, 1003, and 1032 cm-1 (conformational change in proteins and/or bonds at sites close to the aromatic ring of amino acids) occurred in the Cancer group, and the peaks at 451 cm-1 (tryptophan) and 1441 cm-1 (lipids) were higher for the Healthy group. The PLS-DA classified the serum spectra in Healthy and Cancer groups with high accuracy (78%).

Chemometrics and analytical blank on the at-line monitoring of Zika-VLP production using near-infrared spectroscopy

The Zika disease caused by the Zika virus was declared a Public Health Emergency by the World Health Union (WHO), with microcephaly as the most critical consequence. Aiming to reduce the spread of the virus, biopharmaceutical organizations invest in vaccine research and production, based on multiple platforms. A crescent vaccine production approach is based on virus-like particles (VLP), for not having genetic material in its composition, hypoallergenic and non-mutant character. For bioprocess, it is essential to have means of real-time monitoring, which can be assessed using process analysis techniques such as Near-infrared (NIR) spectroscopy, that can be combined with chemometric methods, like Partial-Least Squares (PLS) and Artificial Neural Networks (ANN) for prediction of biochemical variables. This work proposes a biochemical Zika VLP upstream production at-line monitoring model using NIR spectroscopy comparing sampling conditions (with or without cells), analytical blank (air, ultrapure water), and spectra pre-processing approaches. Seven experiments in a benchtop bioreactor using recombinant baculovirus/Sf9 insect cell platform in serum-free medium were performed to obtain biochemical and spectral data for chemometrics modeling (PLS and ANN), composed by a random data split (80 % calibration, 20 % validation) for cross-validation of the PLS models and 70 % training, 15 % testing, 15 % validation for ANN. The best models generated in the present work presented an average absolute error of 1.59 × 105 cell/mL for density of viable cells, 2.37 % for cell viability, 0.25 g/L for glucose, 0.007 g/L for lactate, 0.138 g/L for glutamine, 0.18 g/L for glutamate, 0,003 g/L for ammonium, and 0.014 g/L for potassium.

Laser wavelength and sample conditioning effects on biochemical monitoring of SARS-CoV-2 VLP production upstream stage by Raman spectroscopy

This work assessed the impact of laser wavelength and sample conditioning on offline monitoring (viable cell density, cell viability, virus titer, glucose, lactate, glutamine, glutamate, and ammonium) of SARS-CoV-2 viruslike particles production upstream stage by Raman spectroscopy. The evaluated chemometrics techniques were Partial Least Squares (PLS) and Artificial Neural Networks (ANN), and different spectral filtering approaches were also considered. ANN showed better prediction capacity for most of the parameters, but ammonium and lactate, better predicted by PLS, and glutamine, no difference between modeling techniques was detected. For cell growth parameters and virus titer, samples without cells measured at 785 nm Raman laser wavelength originated better-adjusted models. This laser wavelength was also more appropriate for the remaining monitored experimental parameters except for glucose, in which the best model came from the spectral database acquired at 1064 nm wavelength. Cell remotion significantly increased the accuracy of viable cell density, cell viability, glutamate, and virus titer models. However, glucose, lactate, and ammonium models showed better prediction capacity for samples containing cells. Thus, it was demonstrated that laser wavelength, sample conditioning, spectral preprocessing, and chemometric modeling techniques need to be tailored for each experimental parameter to improve accuracy.

Diagnosing COVID-19 in nasopharyngeal secretion through Raman spectroscopy: a feasibility study.

Since the beginning of the COVID-19 pandemic, the scientific community has sought to develop fast and accurate techniques for detecting the SARS-CoV-2 virus. Raman spectroscopy is a promising technique for diagnosing COVID-19 through serum samples. In the present study, the diagnosis of COVID-19 through nasopharyngeal secretion has been proposed. Raman spectra from nasopharyngeal secretion samples (15 Control, negative and 12 COVID-19, positive, assayed by immunofluorescence antigen test) were obtained in triplicate in a dispersive Raman spectrometer (830 nm, 350 mW), accounting for a total of 80 spectra. Using principal component analysis (PCA) the main spectral differences between the Control and COVID-19 samples were attributed to N and S proteins from the virus in the COVID-19 group. Features assigned to mucin (serine, threonine and proline amino acids) were observed in the Control group. A binary model based on partial least squares discriminant analysis (PLS-DA) differentiated COVID-19 versus Control samples with accuracy of 91%, sensitivity of 80% and specificity of 100%. Raman spectroscopy has a great potential for becoming a technique of choice for rapid and label-free evaluation of nasopharyngeal secretion for COVID-19 diagnosis.

A model based on a multivariate classification for assessing impacts on water quality in a DOCE river watershed after the Fundão tailings dam failure.

The main purpose of this study was to build multivariate classification models using water quality monitoring data for the hydrographic basin of the Gualaxo do Norte River, Minas Gerais state, Brazil, which was impacted in 2015 by the rupture of a containment structure for iron ore tailings. A total of 27 points were evaluated, covering areas affected and unaffected by the disaster, with monitoring of chemical, physical, and microbiological variables during the period from July 2016 to June 2017. Multivariate classification techniques were applied to the data, with the aim of developing models to determine when the impacted locations would present characteristics equivalent to those existing prior to the rupture. Classification models constructed using PLS-DA and LDA were able to predict three classes: unaffected main river, affected main river, and tributaries. The first technique was able to clearly differentiate the three classes for the data evaluated, achieving averages corresponding to 90% accuracy. The second method was consistent with the first, identifying the chloride content, conductivity, turbidity, and alkalinity as discriminatory variables, among those monitored, with the relationships among the parameters being coherent with the environmental conditions of the region. The model, with a correct classification rate of 91.67%, enabled identification of the behavior of new samples, using only these easily measured variables. In summary, application of the multivariate statistical tools allowed the development of models capable of providing information about the recovery process of an ecosystem impacted by the greatest environmental disaster to have occurred in Brazil.

An improved successive projections algorithm version to variable selection in multiple linear regression.

The aim of the successive projections algorithm (SPA) is to enhance the accuracy of multiple linear regressions (MLR) by minimizing the impact of collinearity effects in the calibration data set. Combining SPA with MLR as a variable selection approach has resulted in the SPA-MLR method, which has been reported in literature to produce models with good prediction ability compared to conventional full-spectrum models obtained with partial-least-squares (PLS) in some cases. This paper proposes the addition of a filter step to the current version of the SPA algorithm to reduce the number of uninformative variables before the projection phase and assist the algorithm in selecting the best variables on subsequent steps. The proposed fSPA-MLR algorithm is evaluated in two case studies involving the near-infrared spectrometric analysis of pharmaceutical tablet and diesel/biodiesel mixture samples. Compared to PLS, the fSPA-MLR models demonstrate similar or better performance. Moreover, the fSPA-MLR models outperform the original SPA-MLR in both cross-validation and external prediction. The fSPA-MLR models deliver superior results regardless of the pre-processing algorithm tested, including first-derivative Savitzky-Golay (SG) and Standard Normal Variate (SNV), or even in raw spectra data.

Application of Near-Infrared Reflectance Spectroscopy for Predicting Damage Severity in a Diverse Panel of Tectona grandis Caused by Ceratocystis fimbriata.

Tectona grandis Linn., also known as teak, is a highly valued species with adaptability to a wide range of climatic conditions and high tolerance to soil variations, making it an attractive option for both commercial and conservation purposes. In this sense, the classification of cultivated teak genotypes is crucial for both breeding programs and conservation efforts. This study examined the relationship between traits related to damage in the stem of teak plants caused by Ceratocystis fimbriata (a soil-borne pathogen that negatively impacts the productivity of teak plantations) and the spectral reflectance of 110 diverse clones, using near-infrared spectroscopy (NIRS) data and partial least squares regression (PLSR) analysis. Cross-validation models had R2 = 0.894 (ratio of standard error of prediction to standard deviation: RPD = 3.1), R2 = 0.883 (RPD = 2.7), and R2 = 0.893 (RPD = 2.8) for predicting stem lesion area, lesion length, and severity of infection, respectively. Teak genotypes (clones) can benefit from the creation of a calibration model utilizing NIRS-generated data paired with PLSR, which can effectively screen the magnitude of damage caused by the fungus. Overall, while the study provides valuable information for teak breeding and conservation efforts, a long-term perspective would be essential to evaluate the sustainability of teak genotypes over various growth stages and under continuous pathogen pressure.

Dietary patterns, obesity markers and leukocyte telomere length among Brazilian civil servants: cross-sectional results from the Pro-Saude study.

OBJECTIVE: Dietary patterns express the combination and variety of foods in the diet. The partial least squares method allows extracting dietary patterns related to a specific health outcome. Few studies have evaluated obesity-related dietary patterns associated with telomeres length. This study aims to identify dietary patterns explaining obesity markers and to assess their association with leukocyte telomere length (LTL), a biological marker of the ageing process. DESIGN: Cross-sectional study. SETTING: University campuses in the state of Rio de Janeiro, Brazil. PARTICIPANTS: 478 participants of a civil servants' cohort study with data on food consumption, obesity measurements (total body fat, visceral fat, BMI, leptin and adiponectin) and blood samples. RESULTS: Three dietary patterns were extracted: (1) fast food and meat; (2) healthy and (3) traditional pattern, which included rice and beans, the staple foods most consumed in Brazil. All three dietary patterns explained 23·2 % of food consumption variation and 10·7 % of the obesity-related variables. The fast food and meat pattern were the first factor extracted, explaining 11-13 % variation of the obesity-related response variables (BMI, total body fat and visceral fat), leptin and adiponectin showed the lowest percentage (4·5-0·1 %). The healthy pattern mostly explained leptin and adiponectin variations (10·7 and 3·3 %, respectively). The traditional pattern was associated with LTL (ß = 0·0117; 95 % CI 0·0001, 0·0233) after adjustment for the other patterns, age, sex, exercise practice, income and energy intake. CONCLUSION: Leukocyte telomere length was longer among participants eating a traditional dietary pattern that combines fruit, vegetables and beans.

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach.

Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500-600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440-485 nm) and red (626-700 nm) regions had a minor impact. Strong correlations were found between absorbance (R2 = 0.87 and 0.91) and reflectance (R2 = 0.80 and 0.78) for chlorophyll and carotenoids, respectively. Carotenoids showed particularly high and significant correlation coefficients using the partial least squares regression (PLSR) method (R2C = 0.91, R2cv = 0.85, and R2P = 0.90) when associated with hyperspectral absorbance data. Our hypothesis was supported, and these results demonstrate the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments using multivariate statistical methods. This method for two sensors is more efficient and shows better results compared to traditional single sensor techniques for measuring chloroplast changes and pigment phenotyping in plants.

Development and validation of regression models from NIR spectra to predict the composition of sugarcane, soybean meal, and cornmeal / Construção e validação de modelos de regressão a partir de espectros NIR para predição da composição da cana-de-açúcar, farelo de soja e fubá de milho

This study aimed to develop and assess regression models for predicting the chemical composition of sugarcane, soybean meal, and cornmeal using portable near-infrared (NIR) spectroscopy combined with chemometric techniques. A total of 95 sugarcane samples, 92 soybean meal samples, and 120 cornmeal samples were used. The samples were ground, and NIR spectra were obtained for each sample. Reference values were determined through conventional chemical analysis. Partial least squares regression and leave-one-out cross-validation were employed to construct the models. Models with the lowest root mean squared error in cross-validation were further validated externally. The goodness-of-fit of the models was evaluated by comparing the predicted values with those obtained through conventional laboratory methods. The constructed models properly estimated all constituents evaluated for sugarcane, soybean meal, and cornmeal (P ≥ 0.056). The models developed for predicting the contents of samples oven-dried at 55 °C (ADS) and 105 °C (ODS), total dry matter (DM), organic matter (OM), neutral detergent fiber (NDF), NDF corrected for ash and protein (NDFap), neutral detergent insoluble protein (NDIP), acid detergent fiber (ADF), crude protein (CP), non-fiber carbohydrates (NFC), and total digestible nutrients (TDN) in sugarcane; ODS, OM, NDF, ADF, indigestible NDF (iNDF), CP, TDN, and starch in soybean meal; and ODS and CP in cornmeal exhibited high accuracy and precision (R2 ≥ 0.50 and CCC ≥ 0.60). However, the models developed for predicting the levels of neutral detergent insoluble ash (NDIA) in sugarcane; ether extract (EE) and NDIA in soybean meal; and NDF, iNDF, NDIA, NFC, and EE in cornmeal demonstrated accuracy but lacked precision (R2 ≥ -0.04 and CCC ≥ 0.03). In conclusion, the portable NIR regression models provided accurate estimates and are therefore recommended for predicting the chemical composition of sugarcane, soybean meal, and cornmeal.(AU)

Objetivou-se desenvolver e avaliar modelos de regressão para a predição da composição química da cana-de-açúcar, farelo de soja e fubá de milho por NIR portátil aliado a técnicas quimiométricas. Foram utilizadas 95 amostras de cana-de-açúcar, 92 amostras de farelo de soja e 120 amostras de fubá de milho. Após a moagem das amostras, foi realizada aquisição dos espectros de cada amostra. Os valores referência foram obtidos através de análises químicas convencionais. Para construção dos modelos, foi utilizada a regressão por quadrados mínimos parciais e a validação cruzada leave one out. Os modelos com menor raiz quadrada do erro quadrático médio da validação cruzada foram submetidos a validação externa. Para avaliar a qualidade de ajuste dos modelos, os valores preditos foram comparados com os valores obtidos pelos métodos laboratoriais convencionais. Os modelos construídos estimaram corretamente todos os constituintes avaliados para a cana-de-açúcar, farelo de soja e fubá de milho (P ≥ 0,056). Os modelos construídos para predição dos teores de amostra seca em estufa a 55°C (ASA) e a 105°C (ASE), matéria seca total (MS), matéria orgânica (MO), fibra insolúvel em detergente neutro (FDN), FDN corrigida para cinzas e proteína (FDNcp), proteína insolúvel em detergente neutro (PIDN), fibra insolúvel em detergente ácido (FDA), proteína bruta (PB), carboidratos não fibrosos (CNF) e nutrientes digestíveis totais (NDT) da cana-de-açúcar; ASE, MO, FDN, FDA, FDN indigestível (FDNi), PB, NDT e amido de farelo de soja; e ASE, PB do fubá de milho apresentaram elevada acurácia e precisão (R2 ≥ 0,50 e CCC ≥ 0,60). Contudo os modelos construídos para predição dos teores de cinzas insolúveis em detergente neutro (CIDN) da cana-de-açúcar; extrato etéreo (EE) e CIDN do farelo de soja; e FDN, FDNi, CIDN, CNF e EE do fubá de milho foram acurados, porém pouco precisos (R2 ≥ -0,04 e CCC ≥ 0,03). Conclui-se que os modelos de regressão por NIR portátil estimaram acuradamente e, portanto, são recomendados para estimar a composição química da cana-de-açúcar, farelo de soja e fubá de milho.(AU)

Discrimination of dyslipidemia types with ATR-FTIR spectroscopy and chemometrics associated with multivariate analysis of the lipid profile, anthropometric, and pro-inflammatory biomarkers.

BACKGROUND: Obesity, dyslipidemia, and low-grade inflammatory state form a triad of self-sustaining metabolic dysfunction. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy is a simple, rapid, and non-destructive technique that generates spectral fingerprints of biomolecules that can be correlated with metabolic changes. We verified the efficiency of ATR-FTIR spectroscopy in blood plasma (n = 74) to discriminate the types of dyslipidemias and suggest metabolic inflammatory changes. METHODS: Principal Component Analysis (PCA) was performed on the biochemical and anthropometric data to verify whether the dyslipidemia types share a similar biochemical profile plausible of discrimination in chemometric modeling. To discriminate the types of dyslipidemias based on spectral data, Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) was used and validated with leave-one-out cross-validation. RESULTS: Although no significant difference was obtained between the types of dyslipidemia and normal subjects by CRP, leptin, and cfDNA, there was a significant difference between normal subjects vs combined hyperlipidemia (CH) + hypercholesterolemia (HCL) + hypertriglyceridemia (HTG) (p < 0.05) by the 1245 cm-1 peak [νas(PO2-)] (possible indication of chronic inflammation by increased cfDNA). The area under the curve of the region between 1770 and 1720 cm-1 was significantly increased for CH in relation to other dyslipidemias and normal subjects. Furthermore, there were significant differences for the main representative peaks of lipids, proteins, carbohydrates, and nucleic acids between the types of dyslipidemias and between the types of dyslipidemias and normal subjects. The OPLS-DA model achieved 100 % accuracy with 1 latent variable and Standard Error of Cross-Validation (SECV) < 0.004 for all types of dyslipidemia  and the control group. CONCLUSIONS: Our results suggest that ATR-FTIR spectroscopy associated with chemometric modeling is a plausible applicant for screening the types of dyslipidemias. However, more extensive studies should be conducted to verify the real applicability in clinical analysis laboratories or medical clinics.

Monitoring by Raman spectroscopy of rabies virus-like particles production since the initial development stages

BACKGROUND This study aimed to establish chemometric models using Raman spectroscopy data for biochemical monitoring of rabies Virus-Like Particles (VLP) production based on baculovirus/insect cell system. The models were developed using fresh and stored samples from the initial development stages (Schott culture flasks). The following modeling techniques were assessed: partial least squares (PLS) and artificial neural networks (ANN). The effects of spectral filtering approaches, spectral ranges (400–1850 cm−1; 100–3425 cm−1), and sample cryopreservation were also considered. The applicability of the models was evaluated using experimental data from assays carried out in a benchtop bioreactor. RESULTS The results showed that the prediction capacity of the chemometric models was negatively impacted when samples from rabies VLP production were cryopreserved. Further studies are needed to confirm the maximum storage time for samples (< 4 months) without a significant difference in model predictions compared to those from an in line database. The dilution of the sample should be kept constant throughout the rabies VLP development stages. A nonlinear correlation was observed between dilution and the predicted values of biochemical parameters from Raman spectral data. The choice of spectral filtering has a major impact on the prediction accuracy of chemometric models. CONCLUSION The optimal filtering approach should be individually optimized for each biochemical parameter. The ANN models were significantly more suitable for biochemical monitoring than the PLS approach. The 400–1850 cm−1 Raman shift range is recommended for biochemical monitoring of rabies VLP using a baculovirus/insect cell platform when samples are cell-free.

Partial Least Squares Enhances Genomic Prediction of New Environments.

In plant breeding, the need to improve the prediction of future seasons or new locations and/or environments, also denoted as "leave one environment out," is of paramount importance to increase the genetic gain in breeding programs and contribute to food and nutrition security worldwide. Genomic selection (GS) has the potential to increase the accuracy of future seasons or new locations because it is a predictive methodology. However, most statistical machine learning methods used for the task of predicting a new environment or season struggle to produce moderate or high prediction accuracies. For this reason, in this study we explore the use of the partial least squares (PLS) regression methodology for this specific task, and we benchmark its performance with the Bayesian Genomic Best Linear Unbiased Predictor (GBLUP) method. The benchmarking process was done with 14 real datasets. We found that in all datasets the PLS method outperformed the popular GBLUP method by margins between 0% (in the Indica data) and 228.28% (in the Disease data) across traits, environments, and types of predictors. Our results show great empirical evidence of the power of the PLS methodology for the prediction of future seasons or new environments.

Multi-trait genome prediction of new environments with partial least squares.

The genomic selection (GS) methodology proposed over 20 years ago by Meuwissen et al. (Genetics, 2001) has revolutionized plant breeding. A predictive methodology that trains statistical machine learning algorithms with phenotypic and genotypic data of a reference population and makes predictions for genotyped candidate lines, GS saves significant resources in the selection of candidate individuals. However, its practical implementation is still challenging when the plant breeder is interested in the prediction of future seasons or new locations and/or environments, which is called the "leave one environment out" issue. Furthermore, because the distributions of the training and testing set do not match, most statistical machine learning methods struggle to produce moderate or reasonable prediction accuracies. For this reason, the main objective of this study was to explore the use of the multi-trait partial least square (MT-PLS) regression methodology for this specific task, benchmarking its performance with the Bayesian Multi-trait Genomic Best Linear Unbiased Predictor (MT-GBLUP) method. The benchmarking process was performed with five actual data sets. We found that in all data sets the MT-PLS method outperformed the popular MT-GBLUP method by 349.8% (under predictor E + G), 484.4% (under predictor E + G + GE; where E denotes environments, G genotypes and GE the genotype by environment interaction) and 15.9% (under predictor G + GE) across traits. Our results provide empirical evidence of the power of the MT-PLS methodology for the prediction of future seasons or new environments. Furthermore, the comparison between single univariate-trait (UT) versus MT for GBLUP and PLS gave an increase in prediction accuracy of MT-GBLUP versus UT-GBLUP, but not for MT-PLS versus UT-PLS.