Near Infrared Spectroscopy Spectra Research Articles

Quality Assessment of Reconstructed Cow, Camel and Mare Milk Powders by Near-Infrared Spectroscopy and Chemometrics.

Milk powders are becoming a major attraction for many industrial applications due to their nutritional and functional properties. Different types of powdered milk, each with their own distinct chemical compositions, can have different functionalities. Consequently, the development of rapid monitoring methods is becoming an urgent task to explore and expand their applicability. Lately, there is growing emphasis on the potential of near-infrared spectroscopy (NIRS) as a rapid technique for the quality assessment of dairy products. In the present work, we explored the potential of NIRS coupled with chemometrics for the prediction of the main functional and chemical properties of three types of milk powders, as well as their important processing parameters. Mare, camel and cow milk powders were prepared at different concentrations (5%, 10% and 12%) and temperatures (25 °C, 40 °C and 65 °C), and then their main physicochemical attributes and NIRS spectra were analyzed. Overall, high accuracy in both recognition and prediction based on type, concentration and temperature was achieved by NIRS-based models, and the quantification of quality attributes (pH, viscosity, dry matter content, fat content, conductivity and individual amino acid content) also resulted in high accuracy in the models. R2CV and R2pr values ranging from 0.8 to 0.99 and 0.7 to 0.98, respectively, were obtained by using PLSR models. However, SVR models achieved higher R2CV and R2pr values, ranging from 0.91 to 0.99 and 0.80 to 0.99, respectively.

Application of near-infrared spectroscopy as at line method for the evaluation of histamine in tuna fish (Thunnus albacares)

The assessment of histamine levels in fishery products has emerged as a paramount issue in the context of global food safety, given its profound implications for human health and the consequential impact on food quality and trade. Histamine intoxication, stemming from the ingestion of foods containing heightened histamine levels, results from the bacterial decarboxylation of histidine under conditions of improper handling, processing, or storage. This study endeavors to provide a thorough examination of histamine contamination in frozen-thawed tuna (Thunnus albacares) samples, employing an integrated approach that combines near-infrared spectroscopy (NIRS) with advanced machine learning techniques. One hundred and one samples were considered, and a systematic fortification process was applied to obtain samples with 4 histamine concentrations (0; 50; 150; 250 mg/kg); the fortification levels were confirmed by the LC-MS/MS analysis. Subsequently, NIRS spectra were collected and chemometric analyses, including modified partial least squares regression (MPLS) and support vector machine (SVM), were employed for quantitative and qualitative evaluation, respectively. Histamine quantification through MPLS utilizing the full spectrum exhibited good predictive performance in cross-validation and in hold-out validation (R2CV = 0.88; R2P = 0.74, respectively), confirming the potential of NIRS for estimating histamine levels in tuna. SVM classification models, both binary (presence/absence) and multiclass (four levels), demonstrated high accuracy (100% and 93%, respectively). The study highlights the effectiveness of NIRS combined with machine learning for rapid and accurate histamine detection in frozen-thawed tuna, offering a non-destructive, environmentally friendly alternative to traditional methods. This approach holds significant promise for food business operators and regulatory authorities, enhancing product safety, quality control, and decision-making processes related to histamine contamination in the seafood industry.

Machine learning supported ground beef freshness monitoring based on near‐infrared and paper chromogenic array

AbstractMaintaining freshness and quality is crucial in the meat industry, as lipid oxidation can lead to undesirable odors, flavors, and potential health risks. Traditional methods for assessing meat freshness often involve time‐consuming and destructive techniques, highlighting the need for rapid, noninvasive approaches. Recent advancements in spectroscopic and chromogenic sensor array technologies have opened up new avenues for monitoring meat quality parameters, offering the potential for real‐time, accurate, and cost‐effective solutions. As thiobarbituric acid reactive substances (TBARS) value is a classic indicator of meat lipid oxidation, this study investigated the data fusion of near‐infrared spectroscopy (NIR) and paper chromogenic array (PCA) for monitoring ground beef TBARS. A standardized PCA was fabricated by photolithography with nine chemoresponsive dyes. Changes in ground beef volatile organic compounds during storage were captured in the shifts of PCA color patterns. Nippy, an open‐source Python module, was used for automated NIR spectra preprocessing. The optimal preprocessing pipeline was found by 10‐fold cross‐validation in machine learning model development. Among optimized models, partial least square regression showed the best performance in coefficient of determination (R2) of .9477, root mean squared error of prediction of 0.0545 mg malondialdehyde/kg meat, and residual prediction deviation of 4.3717. The promising result of this study indicated the potential for NIR and PCA combinations to monitor TBARS values for ground beef freshness assessment.

Density profile and NIRs spectra of boron-densified wood at different compression ratios

The present study investigates the effect of pre-treatment and compression ratio (CR) on the enhancement of density in three wood species, specifically nyatoh (Palaquium spp.), pisang putih (Mezzettia spp.) and sepetir (Sindora spp.) cultivated in the North Kalimantan Province. The phenomenon occurred during those treatments was analyzed using X-ray Densitometer and Near Infrared Spectroscopy (NIRs). The wood was subjected to a pre-treatment process involving hot immersion within 2 and 5% of boron solutions at 80 °C for 3 hours. Subsequently, a densification process was performed using hot press machine at 30 kg/cm2 of pressure and 160 °C of temperature for 15 minutes with 20% and 40% CR from the initial thickness. The findings indicated a significant increase in density values for densified wood in comparison to untreated wood. The density values for nyatoh, pisang putih, and sepetir exhibited increases of approximately 31.73%, 36.52%, and 35.04%, respectively. The application of a higher CR leads to a more substantial enhancement in wood density, with an increase of approximately 23.08-25.53% and 30.13-47.01% at 20% and 40% of CR, respectively. The density distribution across the thickness exhibited an M-shape gradient, which convinced the surface densification occurred during the process. NIRs spectra absorbance revealed a prominent peak at wavelength 2178 nm for untreated wood, whereas no such peak was observed in the boron-densified wood. This phenomenon associated with the degradation of hemicellulose during the subsequent treatment applied.

Effect of Sample Presentation on the Classification of Black Soldier Fly Larvae Using Near-Infrared Spectroscopy

Black soldier fly larvae (BSFL) (Hermetia illucens) reared on food waste streams are considered a sustainable source of protein in feed livestock diets. Recently, portable near-infrared spectroscopy (NIR) instruments have been assessed to monitor the consistency and quality of food waste streams used to feed black soldier fly larvae. During the application of NIR spectroscopy, sample presentation (e.g., drying, processing, particle size) plays an important role in the accuracy of the models developed (quantitative or qualitative analysis). The objective of this study was to evaluate the effect of sample presentation (number of larvae used during the scanning of BSFL) on the accuracy of classification models developed to trace the food waste stream (e.g., supermarket of childcare) used to feed the larvae. BSFL samples were sourced from two waste streams and scanned as half, 1, 2, or 3 larvae using an NIR portable instrument (MicroNIR, Viavi, Milpitas, California, USA). Principal component analysis (PCA) and linear discriminant analysis (LDA) were used to analyze the NIR data and to classify the samples according to the waste stream. The main differences in the NIR spectra of the BSFL samples associated with the number of larvae scanned were observed around 1200 nm, mainly associated with the C-H overtones (lipids). The classification results showed that high classification rates (>93%) were obtained regardless of the number of larvae scanned, ranging from 93% (using 0.5 larvae) to 100% (using 1, 2, or 3 larvae samples). Overall, the number of larvae scanned had minimal to no effect on the accuracy of the LDA classification models. The present study demonstrated that a portable NIR instrument can be suitable for an initial rapid classification or determination of the origin of the waste stream used to feed the BSFL.

Rapid and non-invasive detection of malaria parasites using near-infrared spectroscopy and machine learning.

Novel and highly sensitive point-of-care malaria diagnostic and surveillance tools that are rapid and affordable are urgently needed to support malaria control and elimination. We demonstrated the potential of near-infrared spectroscopy (NIRS) technique to detect malaria parasites both, in vitro, using dilutions of infected red blood cells obtained from Plasmodium falciparum cultures and in vivo, in mice infected with P. berghei using blood spotted on slides and non-invasively, by simply scanning various body areas (e.g., feet, groin and ears). The spectra were analysed using machine learning to develop predictive models for infection. Using NIRS spectra of in vitro cultures and machine learning algorithms, we successfully detected low densities (<10-7 parasites/μL) of P. falciparum parasites with a sensitivity of 96% (n = 1041), a specificity of 93% (n = 130) and an accuracy of 96% (n = 1171) and differentiated ring, trophozoite and schizont stages with an accuracy of 98% (n = 820). Furthermore, when the feet of mice infected with P. berghei with parasitaemia ≥3% were scanned non-invasively, the sensitivity and specificity of NIRS were 94% (n = 66) and 86% (n = 342), respectively. These data highlights the potential of NIRS technique as rapid, non-invasive and affordable tool for surveillance of malaria cases. Further work to determine the potential of NIRS to detect malaria in symptomatic and asymptomatic malaria cases in the field is recommended including its capacity to guide current malaria elimination strategies.

An attempt to identify milk protein fraction genotypes using unsupervised and supervised near-infrared spectroscopy methods

The aim was to evaluate near-infrared spectroscopy (NIRS) potential to discriminate among β-casein (CN), κ-CN and β-lactoglobulin (LG) genotypes to be used as an authentication method. A total of 168 milk samples with known genetic information for β-CN, κ-CN and β-LG were collected at the same farm and paired with the NIRS spectrum. Spectra were evaluated with an unsupervised method (principal component analysis, PCA) and a supervised method (partial least squares-discriminant analysis, PLS-DA). For the PLS-DA, data were split into a train (75%) and a test set (25%), and the variable in projection >1 criterion was applied to select informative wavelengths. Results obtained confirmed that milk quality was similar among genetic variants. For the PCA, the observed variance explained by the first two principal components was 94%, but samples were not clustered by their genotypes of β-CN (i.e. A1A2, A2A2), κ-CN (i.e. AA, AB, AE, BB, BE) and β-LG (i.e. AA, AB, BB). The best accuracy for the PLS-DA models was reached by β-CN (train and test set, 64%), followed by β-LG (train set, 56%; test set, 52%) and κ-CN (train set, 41%; test set, 36%). In conclusion, the PCA on milk spectra was not able to cluster β-CN, κ-CN and β-LG genotypes, but the PLS-DA models revealed promising results for β-CN and β-LG. It could be interesting to increase the number of samples to equilibrate genetic variants and to apply a sampling selection method before discarding the applicability of NIRS as an authentication method.

Predicting ruminal degradability and chemical composition of corn silage using near-infrared spectroscopy and multivariate regression.

The aim of this study was to develop and validate regression models to predict the chemical composition and ruminal degradation parameters of corn silage by near-infrared spectroscopy (NIR). Ninety-four samples were used to develop and validate the models to predict corn silage composition. A subset of 23 samples was used to develop and validate models to predict ruminal degradation parameters of corn silage. Wet chemistry methods were used to determine the composition values and ruminal degradation parameters of the corn silage samples. The dried and ground samples had their NIR spectra scanned using a poliSPECNIR 900-1700 model NIR sprectrophotometer (ITPhotonics S.r.l, Breganze, IT.). The models were developed using regression by partial least squares (PLS), and the ordered predictor selection (OPS) method was used. In general, the regression models obtained to predict the corn silage composition (P>0.05), except the model for organic matter (OM), adequately estimated the studied properties. It was not possible to develop prediction models for the potentially degradable fraction in the rumen of OM and crude protein and the degradation rate of OM. The regression models that could be obtained to predict the ruminal degradation parameters showed correlation coefficient of calibration between 0.530 and 0.985. The regression models developed to predict CS composition accurately estimated the CS composition, except the model for OM. The NIR has potential to be used by nutritionists as a rapid prediction tool for ruminal degradation parameters in the field.

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia.

The use of non-invasive tools in conjunction with artificial intelligence (AI) to detect diseases has the potential to revolutionize healthcare. Near-infrared spectroscopy (NIR) is a technology that can be used to analyze biological samples in a non-invasive manner. This study evaluated the use of NIR spectroscopy in the fingertip to detect neutropenia in solid-tumor oncologic patients. A total of 75 patients were enrolled in the study. Fingertip NIR spectra and complete blood counts were collected from each patient. The NIR spectra were pre-processed using Savitzky-Golay smoothing and outlier detection. The pre-processed data were split into training/validation and test sets using the Kennard-Stone method. A toolbox of supervised machine learning classification algorithms was applied to the training/validation set using a stratified 5-fold cross-validation regimen. The algorithms included linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), and support vector machines (SVMs). The SVM model performed best in the validation step, with 85% sensitivity, 89% negative predictive value (NPV), and 64% accuracy. The SVM model showed 67% sensitivity, 82% NPV, and 57% accuracy on the test set. These results suggest that NIR spectroscopy in the fingertip, combined with machine learning methods, can be used to detect neutropenia in solid-tumor oncology patients in a non-invasive and timely manner. This approach could help reduce exposure to invasive tests and prevent neutropenic patients from inadvertently undergoing chemotherapy.

Portable NIR spectroscopy: the route to green analytical chemistry.

There is a growing interest for cost-effective and nondestructive analytical techniques in both research and application fields. The growing approach by near-infrared spectroscopy (NIRs) pushes to develop handheld devices devoted to be easily applied for in situ determinations. Consequently, portable NIR spectrometers actually result definitively recognized as powerful instruments, able to perform nondestructive, online, or in situ analyses, and useful tools characterized by increasingly smaller size, lower cost, higher robustness, easy-to-use by operator, portable and with ergonomic profile. Chemometrics play a fundamental role to obtain useful and meaningful results from NIR spectra. In this review, portable NIRs applications, published in the period 2019-2022, have been selected to indicate starting references. These publications have been chosen among the many examples of the most recent applications to demonstrate the potential of this analytical approach which, not having the need for extraction processes or any other pre-treatment of the sample under examination, can be considered the "true green analytical chemistry" which allows the analysis where the sample to be characterized is located. In the case of industrial processes or plant or animal samples, it is even possible to follow the variation or evolution of fundamental parameters over time. Publications of specific applications in this field continuously appear in the literature, often in unfamiliar journal or in dedicated special issues. This review aims to give starting references, sometimes not easy to be found.

Turning Zr(IV) into a phosphate ester mimetic enzyme via de novo synthesis for hydrolyzing organophosphorus warfare agents and rapid activity prediction by near-infrared spectroscopy

Chemical warfare agents, especially organophosphorus warfare agents are a variety of various chemical substances employed in warfare that can massively cause damage to the organism even death. The partial catalytic function of Universitetet i Oslo-66 (UiO-66, a zirconium-based metal–organic framework) produced via de novo synthesis is similar to calf intestinal alkaline phosphatase (CIAP, EC 3.1.3.1) and has a lower usage cost, is expected to be a mimetic enzyme for hydrolyzing organophosphorus warfare agents due to the fact that it has lattice defects. 0.1–5.6 mL of acetic acid was added as a regulator to form different lattice defects. Typically, mimetic enzyme activity tests often require expensive reagents as substrates, and the experiment processes are time-consuming and inconvenient. As a result, a novel approach based on near-infrared spectroscopy (NIRs) was developed in this research for predicting UiO-66 mimetic enzyme activity. p-Nitrophenyl phosphate disodium salt (pNPP) was selected as a lower toxic organophosphorus warfare agent to conduct the subsequent simulant research. The absorbance of the mimetic enzyme catalytic system was evaluated using Ultraviolet–Visible (UV–Vis) spectroscopy to investigate the amount of its activity and compare it with CIAP activity. The synthesized samples’ enzyme activities ranged from 1.91 to 6.67 U/g and their catalytic costs ranged from 0.65 to 2.26 CNY. The powdered samples' near-infrared spectra were obtained. The sample’s enzyme activity data were linked with each sample’s original NIR spectra to establish the NIR model. A partial least squares (PLS) model of mimetic enzyme activity was developed by coupling spectral preprocessing with a variable screening technique. The root-mean-square error of cross-validation (RMSECV), the correlation coefficient of validation set (R) value, and the ratio of prediction to deviation (RPD) value were employed as assessment model indicators. The near-infrared spectrum model was developed by merging the best 2nd derivative spectral preprocessing with the Competitive Adaptive Reweighted Sampling (CARS) variable screening method. This model's RMSECV was 0.41 U/g, the correlation coefficient of calibration set (R_cv) value was 0.916, the root-mean-square error of prediction (RMSEP) set was 0.52 U/g, R was 0.948, and RPD was 2.51. As a result, the established model could be implemented to quantify the activity of UiO-66 quickly by including more variations of de novo-synthesized samples. The prediction method is simple, rapid, low cost and adaptable to be the theoretical and practical basis for further studying other interdisciplinary research work in biomimetic enzymology and spectroscopy.

NIRS and multivariate methods for discrimination of morning glory species at different growth stages

Morning glory species are weeds very common in tropical crops, where they cause direct and indirect damage. The management of these species primarily relies on the application of herbicides, disregarding the growth stage and spatial distribution. Studies addressing new techniques for identifying these species may contribute to the development of proximal sensors for carrying out specific and rational management. Thus, the objective of this work was to use near infrared spectroscopy (NIRS) and multivariate analysis to discriminate two species of morning glory in three growth stages. NIRS spectra were collected from Ipomoea hederifolia and Merremia aegyptia were collected at three different stages in the spectral range of 4.000 to 10.000 cm-1. PCA and PC-LDA were used to analyze the entire spectrum and specific bands. NIRS associated with PCA and PC-LDA were sufficient to discriminate I. hederifolia and M. aegyptia species and their growth stages. PCA allowed a proper segregation of stages and species when applied individually PC-LDA correctly classified between 90.93 to 100% of species and stages. The best discrimination results were observed in the NIR spectra ranges from 4.500 to 6.000 cm-1 and 4.500 to 6.000 + 6.500 to 7.750 cm-1. This study represents an advance in the research and implementation of NIRS technology to discriminate weed species for the future development of equipment to assist in the adoption and/or performance of a specific management of weeds, capable of contributing to the reduction in the use of herbicides in crops.

Preliminary Feasibility of Near-Infrared Spectroscopy to Authenticate Grazing in Dairy Goats through Milk and Faeces Analysis.

Consumers are increasingly prone to request information about the production systems of the food they buy. For this purpose, certification and authentication methodologies are necessary not only to protect the choices of consumers, but also to protect producers and production systems. The objective of this preliminary work was to authenticate the grazing system of dairy goats using Near-Infrared Spectroscopy (NIRS) analyses of milk and faeces of the animals. Spectral information and several mathematical pre-treatments were used for the development of six discriminant models based on different algorithms for milk and faeces samples. Results showed that the NIRS spectra of both types of samples had some differences when the two feeding regimes were compared. Therefore, good discrimination rates were obtained with both strategies (faeces and milk samples), with classification percentages of up to 100% effectiveness. Discrimination of feeding regime and grazing authentication based on NIRS analysis of milk samples and an alternative sample such as faeces is considered as a potential approach for dairy goats and small ruminant production.

Oxidation of whey protein isolate after thermal convection and microwave heating and freeze-drying: Correlation among physicochemical and NIR spectroscopy analyses

This study investigated the oxidative susceptibility of whey protein isolate (WPI) dispersions treated by microwave or thermal convection before freeze-drying. WPI (20 mg protein/mL) in distilled water (DW) was heated at 63 ± 2 °C for 30 min by microwave (WPI-MW) or convection heating (WPI–CH) and freeze-dried. Untreated WPI (WPI–C), WPI solubilized in DW and freeze-dried (WPI-FD), and WPI solubilized in DW, heated at 98 ± 2 °C for 2 min and freeze-dried (WPI–B) were also evaluated. Structural changes (turbidity, ζ potential, SDS-PAGE, and near-infrared spectroscopy (NIR)) and protein oxidation (dityrosine, protein carbonylation, and SH groups) were investigated. WPI-FD showed alterations compared to WPI-C, mainly concerning carbonyl groups. Microwave heating increased carbonyl groups and dityrosine formation compared to conventional heating. NIR spectrum indicated changes related to the formation of carbonyl groups and PCA analysis allowed us to distinguish the samples according to carbonyl group content. The results suggest that NIR may contribute to monitoring oxidative changes in proteins resulting from processing.

Rapid prediction method of ZIF-8 immobilized Candida rugosa lipase activity by near-infrared spectroscopy

Candida rugosa lipase (CRL, EC3.1.1.3) is one of the main enzymes synthesizing esters, and ZIF-8 was chosen as an immobilization carrier for lipase. Enzyme activity testing often requires expensive reagents as substrates, and the experiment processes are time-consuming and inconvenient. As a result, a novel approach based on near-infrared spectroscopy (NIRs) was developed for predicting CRL/ZIF-8 enzyme activity. The absorbance of the immobilized enzyme catalytic system was evaluated using UV–Vis spectroscopy to investigate the amount of CRL/ZIF-8 enzyme activity. The powdered samples' near-infrared spectra were obtained. The sample’s enzyme activity data were linked with each sample’s original NIR spectra to establish the NIR model. A partial least squares (PLS) model of immobilized enzyme activity was developed by coupling spectral preprocessing with a variable screening technique. The experiments were completed within 48 h to eliminate inaccuracies between the reduction in enzyme activity with increasing laying-aside time throughout the test and the NIRs modeling. The root-mean-square error of cross-validation (RMSECV), the correlation coefficient of validation set (R) value, and the ratio of prediction to deviation (RPD) value were employed as assessment model indicators. The near-infrared spectrum model was developed by merging the best 2nd derivative spectral preprocessing with the Competitive Adaptive Reweighted Sampling (CARS) variable screening method. This model's root-mean-square error of cross-validation (RMSECV) was 0.368 U/g, the correlation coefficient of calibration set (R_cv) value was 0.943, the root-mean-square error of prediction (RMSEP) set was 0.414 U/g, the correlation coefficient of validation set (R) value was 0.952, and the ratio of prediction to deviation (RPD) was 3.0. The model demonstrates that the fitting relationship between the predicted and the reference enzyme activity value of the NIRs is satisfactory. The findings revealed a strong relationship between NIRs and CRL/ZIF-8 enzyme activity. As a result, the established model could be implemented to quantify the enzyme activity of CRL/ZIF-8 quickly by including more variations of natural samples. The prediction method is simple, rapid, and adaptable to be the theoretical and practical basis for further studying other interdisciplinary research work in enzymology and spectroscopy.

In field study on immune-genes expression during a lactococcosis outbreak in rainbow trout (Oncorhynchus mykiss)

Italian rainbow trout (Oncorhynchus mykiss) farms are periodically affected by outbreaks of lactococcosis caused by Lactococcus garvieae, and rearing basins reveal simultaneously asymptomatic and symptomatic fish. The present study, as part of an “in field” sanitary survey, was designed to describe the expression of immune related genes in infected versus healthy fish, as well as to explore a Near Infrared Spectroscopy (NIR) as a novel approach to discriminate between them. Measurements/sampling were performed in a farm of Northern Italy and included two groups of fish (150–300 g), sharing the same basin: asymptomatic (n = 10) and symptomatic (n = 9). Fingerling (n = 5, 15 g) were also sampled from the farm internal hatchery and considered as L. garvieae-free reference group. NIR spectra were collected from the abdominal region of the fish through the SCiO molecular device. Spleen and head kidney were sampled for bacterial detection (PCR). Head kidney was also subjected to gene expression analysis (qPCR). Seven out of nine (7/9) symptomatic trout were L.garvieae positive (bacterial DNA) while all the asymptomatic (10/10) and fingerling (2/2) were L. garvieae negative. SCiO scanning provided two different spectral populations, almost coinciding with the two fish groups considered (asymptomatic and symptomatic). Among cytokines, IL-1β showed significant up-regulation in symptomatic group compared to asymptomatic group (P < 0.05) while IL-8 showed very high (P < 0.01) and high significant (P < 0.05) differences in its up-regulation in symptomatic group compared to asymptomatic group and fingerling, respectively. IL-10 showed a tendency of significant over-expressed in symptomatic fish compared to fingerling (P < 0.1). TCR-β showed significant (P < 0.05) down-regulation in symptomatic fish compared to asymptomatic ones and MHC-II resulted in down-regulation in a very significant (P < 0.01) manner in symptomatic trout compared to fingerling and asymptomatic. Concerning immunoglobulin genes expression, the level of mRNA transcript of IgT was significantly higher in symptomatic trout compared to fingerling (P < 0.05) and tended to be higher in asymptomatic trout compared to fingerling (P < 0.1). No significant differences were observed for IL-6, TNF-α, TLR5, MHC-I and IgM. In this study, a NIR based approach was used for the first time in the field of fish pathology/immunology with the aim of differentiating between healthy and diseased fish. Moreover, our results on immune related genes modulation highlighted how the outcome of a L. garvieae infection might be influenced by a specific gene expression pattern, which is crucial in the mode of action of trout immune system against L. garvieae.

Direct NIR spectral determination of genetic improvement, light availability, and their interaction effects on chemically selected yerba-mate leaves

Monitoring plant chemical modifications provides useful information about plant genetic variability owing to environmental stresses. This allows determining if the pattern of chemical compounds of interest remains constant. Classically, chemical compounds are monitored through classical analytical procedures that are costly and environmentally harmful. Decaffeinated and high caffeine yerba-mate trees were selected based on their chemical profiles. Their behavior was evaluated at five different shading levels (0%, 40%, 51%, 76%, and 82%), imitating the environmental characteristics of yerba-mate in agroforestry systems. Near-infrared spectroscopy (NIR) fingerprints were obtained for yerba-mate leaves, and ANOVA-simultaneous component analysis (ASCA) models were developed for chemical monitoring. The preprocessing of NIR spectra was assessed with a three-level full factorial design. The best preprocessing set of conditions found was standard normal variate followed by second derivatives, computed according to the Savitzky–Golay method (11-points window and second-degree interpolating polynomial). The ASCA models satisfactorily showed significant differences in chemical behavior for genetic improvement and its interaction with light availibility effects. The genetic improvement effect had a variance of 44.30% over NIR fingerprints, indicating that the chemical differences are maintained at any light level. The interaction effect with 1.84% of the total variance illustrated that the behaviors of decaffeinated and high-caffeinated yerba mate were different when subjected to the same light level. The main absorptions indicated by the ASCA model were the O-H stretching and bending combination band at 1654 nm, and the –CH stretching aromatic 1st overtone at 1675 nm. The ASCA method offered a powerful tool for monitoring chemically selected plants combined with NIR spectroscopy. The method is cheap, fast, environmentally friendly, and can be used for field research.

Follow-up of solid-state fungal wood pretreatment by a novel near-infrared spectroscopy-based lignin calibration model

Lignin removal plays a crucial role in the efficient bioconversion of lignocellulose to fermentable sugars. As a delignification process, fungal pretreatment has gained great interest due to its environmental friendliness and low energy consumption. In our previous study, a positive linear correlation between acid-insoluble lignin degradation and the achievable enzymatic saccharification yield has been found, hereby highlighting the importance of the close follow-up of lignin degradation during the solid-state fungal pretreatment process.However, the standard quantification of lignin, which relies on the two-step acid hydrolysis of the biomass, is highly laborious and time-consuming. Vibrational spectroscopy has been proven as a fast and easy alternative; however, it has not been extensively researched on lignocellulose subjected to solid-state fungal pretreatment. Therefore, the present study examined the suitability of near-infrared spectroscopy (NIR) for the rapid and easy assessment of lignin content in poplar wood pretreated with Phanerochaete chrysosporium. Furthermore, the predictive power of the obtained calibration model and the recently published ATR-FTIR spectroscopy-based model were compared for the first time using the same fungus-treated wood data set.PLSR was used to correlate the NIR spectra to the acid-insoluble lignin contents (19.9%–27.1%) of pretreated wood. After normalization and second derivation, a PLSR model with a good coefficient of determination (RCV2 = 0.89) and a low root mean square error (RMSECV = 0.55%) were obtained despite the heterogeneous nature of the fungal solid-state fermentation. The performance of this PLSR model was comparably good to the one obtained by ATR-FTIR (RCV2 = 0.87) while it required more extensive spectral pre-processing. In conclusion, both methods will be highly useful for the high-throughput and user-friendly monitoring of lignin degradation in a solid-state fungal pretreatment-based biorefinery concept.

Contribution of woody plants to horses’ diets in Mediterranean rangelands

In Mediterranean countries, shrubby and woody areas provide herds with cheap fodder. However, some horse farmers are reluctant to use woody rangelands, which they associate with low-quality feed. In this article, we explore some aspects of the feeding behavior of horses on Mediterranean shrubby and woody rangelands, in order to document their pastoral interest for horses. We observed the feeding choices of young horses grazing on typical peri-Mediterranean rangelands. Observations were repeated at two sites, with four horses per site, over four days with two observers. At each site, two horses were fitted with GPS (positioning system) collars to determine their presence in different areas of the paddocks. Ingested plants were classified in a coding grid. The chemical characteristics of each feed item were estimated by near-infrared spectroscopy (NIRS). A hierarchical classification of NIRS spectra defined four classes. Leaves, twigs, and flowers of several woody species were ingested (1‒18% of recorded bites), although herbaceous species were preferred (&gt; 80% of bites). Horses explored the entire paddocks but grazed preferentially in open areas. The differences observed between sites suggested a strong effect of local grazing conditions. The chemical composition of feed from shrubs and trees was close to that of herbaceous forbs but quite different from that of grasses. Three out of four classes associated woody plants and forbs. These classes had interesting nutritional characteristics (protein &gt; 12%). These results confirm the positive contribution of woody plants to horses' nutrition, in addition to the other roles of woody plants in the well-being of horses on pasture.

Quantification and Prediction with Near Infrared Spectroscopy of Carbohydrates throughout Apple Fruit Development

Carbohydrates play a key role in apple fruit growth and development. Carbohydrates are needed for cell division/expansion, regulate fruitlet abscission, and influence fruit maturation and quality. Current methods to quantify fruit carbohydrates are labor intensive and expensive. We quantified carbohydrates throughout a growing season in two cultivars and evaluated the use of near infrared spectroscopy (NIR) to predict apple carbohydrate content throughout changes in fruit development. Carbohydrates were quantified with high performance liquid chromatography (HPLC) at five timepoints between early fruitlet growth and harvest in ‘Gala’ and ‘Red Delicious’ apples. NIR spectra was collected for freeze-dried fruit samples using a benchtop near infrared spectrometer. Sorbitol was the major carbohydrate early in the growing season (~40% of total carbohydrates). However, the relative contribution of sorbitol to total carbohydrates rapidly decreased by 59 days after full bloom (&lt;10%). The proportion of fructose to total carbohydrates increased throughout fruit development (40–50%). Three distinct periods of fruit development, early, mid-season, and late, were found over all sampling dates using principal component analysis. The first (PC1) and second (PC2) principal components accounted for 90% of the variation in the data, samples separated among sampling date along PC1. Partial least squares regression was used to build the models by calibrating carbohydrates quantified with HPLC and measured reflectance spectra. The NIR models reliably predicted the content of fructose, glucose, sorbitol, sucrose, starch, and total soluble sugars for both ‘Gala’ and ‘Red Delicious’; r2 ranged from 0.60 to 0.96. These results show that NIR can accurately estimate carbohydrates throughout the growing season and offers an efficient alternative to liquid or gas chromatography.