Fourier Transform Mid-infrared Spectroscopy Research Articles

Pregnancy status predicted using milk mid-infrared spectra from dairy cattle

Accurate and timely pregnancy diagnosis is an important component of effective herd management in dairy cattle. Predicting pregnancy from Fourier-transform mid-infrared (FT-MIR) spectroscopy data is of particular interest because the data are often already available from routine milk testing. The purpose of this study was to evaluate how well pregnancy status could be predicted in a large data set of 1,161,436 FT-MIR milk spectra records from 863,982 mixed-breed pasture-based New Zealand dairy cattle managed within seasonal calving systems. Three strategies were assessed for defining the nonpregnant cows when partitioning the records according to pregnancy status in the training population. Two of these used records for cows with a subsequent calving only, whereas the third also included records for cows without a subsequent calving. For each partitioning strategy, partial least squares discriminant analysis models were developed, whereby spectra from all the cows in 80% of herds were used to train the models, and predictions on cows in the remaining herds were used for validation. A separate data set was also used as a secondary validation, whereby pregnancy diagnosis had been assigned according to the presence of pregnancy-associated glycoproteins (PAG) in the milk samples. We examined different ways of accounting for stage of lactation in the prediction models, either by including it as an effect in the prediction model, or by pre-adjusting spectra before fitting the model. For a subset of strategies, we also assessed prediction accuracies from deep learning approaches, utilizing either the raw spectra or images of spectra. Across all strategies, prediction accuracies were highest for models using the unadjusted spectra as model predictors. Strategies for cows with a subsequent calving performed well in herd-independent validation with sensitivities above 0.79, specificities above 0.91 and area under the receiver operating characteristic curve (AUC) values over 0.91. However, for these strategies, the specificity to predict nonpregnant cows in the external PAG data set was poor (0.002-0.04). The best performing models were those that included records for cows without a subsequent calving, and used unadjusted spectra and days in milk as predictors, with consistent results observed across the training, herd-independent validation and PAG data sets. For the partial least squares discriminant analysis model, sensitivity was 0.71, specificity was 0.54 and AUC values were 0.68 in the PAG data set; and for an image-based deep learning model, the sensitivity was 0.74, specificity was 0.52 and the AUC value was 0.69. Our results demonstrate that in pasture-based seasonal calving herds, confounding between pregnancy status and spectral changes associated with stage of lactation can inflate prediction accuracies. When the effect of this confounding was reduced, prediction accuracies were not sufficiently high enough to use as a sole indicator of pregnancy status.

The use of milk Fourier-transform mid-infrared spectroscopy to diagnose pregnancy and determine spectral regional associations with pregnancy in US dairy cows

Accurate early diagnosis of pregnancy is important for timely reproductive management of dairy farms. Fourier-transform mid-infrared (FT-MIR) milk spectral data are routinely used for determining milk components such as fat and protein, whereas milk composition is known to change with advancing stages of pregnancy. The objectives of this study were to compare partial least squares discriminant analysis (PLS-DA) and a Bayesian variable selection regression model (BayesC) for the diagnosis of pregnancy status (PS) from milk FT-MIR data and to infer any spectral regions that might be highly associated with PS at various stages of pregnancy. Conception dates on confirmed pregnant cows were obtained from Holstein cows within 123 herds in Michigan, Ohio, and Indiana during 2018 and 2019. Milk samples from these pregnant cows at 7 different stages of pregnancy were case-control matched to open contemporary herd mates to be within the same stage (±10 d for days in milk) of lactation for the same milk sample test date. The FT-MIR data were obtained for all of these milk samples. Ten-fold herd-independent cross-validation was used to compare PLS-DA versus BayesC using the area under the receiver operating characteristic curve (AUC). The BayesC model demonstrated higher mean AUC compared with PLS-DA at all stages exceeding 60 d of pregnancy. The mean BayesC AUC at stage 1 (1-30 d) was 0.58 ± 0.02, which was superior to a random guess (AUC = 0.50) yet too low to be of practical use. The mean BayesC AUC at stage 7 (≥180 d) was 0.13 greater compared with that of stage 1 (1-30 d) and 0.07 to 0.10 greater compared with stages 2, 3, 4, 5, and 6 (31-180 d in 30-d increments). The mean AUC of stages 2 to 6 were 0.03 to 0.06 greater compared with stage 1 yet again too low to be of practical use. Because of high multicollinearity between many adjacent wavenumbers, a spatially constrained clustering algorithm was used to adaptively partition wavenumbers into 68 windows before inferring associations of spectral regions with pregnancy. Pregnancy status was highly associated with wavenumber windows 1,063 to 1,134 cm-1, 1,201 to 1,257 cm-1, and 1,260 to 1,432 cm-1 based on an estimated BayesC posterior probability of association (PPA) approaching 100% for each of these windows at all pregnancy stages. Other windows ranging from 1,730 to 1,764 cm-1, 1,775 to 1,992 cm-1, 1,995 to 2,163 cm-1, and 2,167 to 2,316 cm-1 had varying medium to high PPA (30% to 100%) across stages. The estimated PPA in wavenumber regions from 1,477 to 1,507 cm-1, and 1,510 to 1,574 cm-1 was weaker in stages 1 and 2 compared with later stages, whereas for the regions 2,984 to 3,077 cm-1 and 3,081 to 3,133 cm-1 the effect of pregnancy was greater for stage 1 compared with other stages. Despite our conclusion that milk FT-MIR data poorly diagnose PS, our study provides new insights into spectral regions that are strongly associated with PS and warrant greater attention.

SEM and FT-MIR Analysis of Human Demineralized Dentin Matrix: An In Vitro Study

Recently, the demineralized dentin matrix has been suggested as an alternative material to autologous bone grafts and xenografts for clinical purposes. The aim of this study was to investigate the effect of different times of demineralization on the chemical composition and the surface morphology of dentinal particles. Extracted teeth were ground and divided into 5 groups based on demineralization time (T0 = 0 min, T2 = 2 min, T5 = 5 min, T10 = 10 min, and T60 = 60 min) with 12% EDTA. The analysis was performed using Fourier-Transform Mid-Infrared spectroscopy (FT-MIR) and Scanning Electron Microscopy (SEM) (p < 0.05). The FT-MIR analysis showed a progressive reduction of the concentration of both PO43− and CO32− in the specimens (T0 > T2 > T5 > T10 > T60). On the contrary, the organic (protein) component did not undergo any change. The SEM examination showed that increasing the times of demineralization resulted in a smoother surface of the dentin particles and a higher number of dentinal tubules.

QUANTITATIVE SPECIATION OF THE LIQUID PHASE BY FTIR SPECTROSCOPY IN THE SYSTEM AMP-PZCO2-H2O

This work reports a multivariate calibration partial least square regression (PLS) model to quantify the liquid-phase concentrations of 2-amino-2-methyl-1-propanol (AMP), piperazine (PZ), bicarbonate, PZ monocarbamate, and PZ dicarbamate during the absorption/ desorption process by Fourier Transform Mid-Infrared spectroscopy (mid-FTIR). The model could predict 33 different concentrations ranging from 0 to 40 wt.% AMP, 0 to 15 wt.% PZ, and 0 to 12 wt.% total CO2 with relative errors lower than 10%, and 87% of the variance of all samples have been represented (R²), except for PZ dicarbamate. A comparison of a single PLS2 model was made with multiple PLS1 models, one for each chemical species present in the liquid phase. The latter had better predictions and made it possible to differentiate CO2 from its chemical forms, allowing a better understanding of the CO2 capture processes.

FT-MIR Analysis of Water-Soluble Extracts during the Ripening of Sheep Milk Cheese with Different Phospholipid Content

The purpose of this work was to study the suitability of the water-soluble extracts (WSE) of semi-hard sheep milk cheese for analysis by diffuse reflectance Fourier transform mid-infrared spectroscopy (FT-MIR) and the development of classification models using discriminant analysis and based on cheese age or phospholipid content. WSE was extracted from three types of sheep milk cheeses (full-fat, reduced-fat and reduced-fat fortified with lyophilized sweet sheep buttermilk) at various stages of ripening from six to 168 days and lyophilized. The first model used 1854–1381 and 1192–760 cm−1 regions of the first-derivative spectra and successfully differentiated samples of different age, based on changes in the water-soluble products of ripening biochemical events. The second model used the phospholipid absorbance spectral regions (3012–2851, 1854–1611 and 1192–909 cm−1) to successfully discriminate cheeses of markedly different phospholipid content. Cheese WSE was found suitable for FT-MIR analysis. According to the results, a fast and simple method to monitor cheese ripening based on water-soluble substances has been developed. Additionally, the results indicated that a considerable amount of phospholipids migrates to the cheese WSE and that FT-MIR can be a useful tool for their assessment.

Identification and quantitative analysis of salt-adulterated honeysuckle using infrared spectroscopy coupled with multi-chemometrics

The quality of honeysuckle is typically degraded seriously by unscrupulous mixing with adulterating substances, especially salt. This study proposes a model based on fourier-transform mid-infrared (Mid-IR) spectroscopy and diode-array near-infrared (NIR) spectroscopy in combination with chemometric techniques for the identification and quantification of salt-adulterated honeysuckle. For model training, Mid-IR and NIR spectra of 72 sample batches (including 32 batches of salt-free samples and 40 batches of salt-adulterated samples) were collected. After that, honeysuckle sample authentication was performed using the partial least squares discriminant analysis (PLS-DA) model based on NIR spectroscopy data. This model was optimized after processing by 28 types of pretreatment schemes, possessed of 100% correct authentication of salt-free and salt-adulterated sample at 0.5%-20% (W/W) levels. Subsequently, PLS regression models were built for quantify salt adulteration levels. These models were optimized by the synergy interval PLS (SiPLS) and backward interval PLS (BiPLS) algorithms. The SiPLS-optimized mid-IR-based PLS model demonstrated the lowest values of two error metrics, namely, a root-mean-square error of estimation (RMSEE) of 2.38%, and a root-mean-square error of prediction (RMSEP) of 3.14%. Then, optimal PLS models were obtained through applying several variable selection methods, including variable importance for projection (VIP), competitive adaptive reweighted sampling (CARS), the successive projection algorithm (SPA), and uninformative variable elimination (UVE). The results indicated that the SiPLS-VIP multi-dimensional analysis methods showed a maximum RMSEP reduction of 1.02% in comparison with models constructed using the BiPLS or SiPLS algorithms alone. In general, the proposed multi-dimensional analysis methods show a great potential to complement existing methodologies for rapid and effective salt adulteration detection in honeysuckle samples.

Biochar ageing in polluted soils and trace elements immobilisation in a 2-year field experiment

Biochar application to soils has become a focus of research during the last decade due to its high potential for C sequestration. Nevertheless, there is no exhaustive information on the long-term effects of biochar application in soils contaminated with trace elements. In this work, a 2-year field experiment was conducted comprising the application of different types of biochar to acidic and moderately acidic soils with high concentrations of As, Cu, Pb, Ba and Zn. In addition, representative samples of each biochar were buried in permeable bags that allowed the flow of water and microorganisms but not their physical interaction with soil aggregates. The biochars significantly adsorbed trace elements from polluted soils. However, given the high total concentration of these persistent trace elements in the soils, the application of biochars did not succeed in reducing the concentration of available metals (CaCl2 extractable fraction). After 2 years of ageing under field conditions, some degradation of the biochars from olive pit, rice husk and wood were observed. This study provides novel information concerning the biochar alterations during ageing in polluted soils, as the decrease of aryl C signal observed by 13C nuclear magnetic resonance (NMR) spectroscopy and the presence of O-containing groups shown by Fourier Transform mid-Infrared Spectroscopy (FT-IR) in aged biochar which enhanced trace elements adsorption. Scanning electron microscopy (SEM) revealed slight changes on surface morphology of aged biochar particles.

Biological Deacidification Strategies for White Wines

Traditionally, the use of malolactic fermentation gives rise to microbiologically stable wines. However, malolactic fermentation is not free from possible collateral effects that can take place under specific scenarios. The present work tests the influence of different biological deacidification strategies on the volatile and non-volatile components of white must from Germany. The study compared mixed cultures of Lachancea thermotolerans and Schizosaccharomyces pombe and a pure culture of Sc. pombe to the classical biological deacidification process performed by lactic acid bacteria. Strains of Oenococcus oeni and Lactiplantibacillus plantarum were co- or sequentially inoculated with S. cerevisiae to carry out malolactic fermentation. Different fermentation treatments took place at a laboratory scale of 0.6 L in vessels of 0.75 L. The instrumental techniques Fourier-transform mid-infrared spectroscopy (FT-MIR), high performance liquid chromatography (HPLC) and gas chromatography–mass spectrometry (GC-MS) were used to evaluate different chemical parameters in the final wines. The results showed the ability of Sc. pombe to consume malic acid in combination with L. thermotolerans without using S. cerevisiae or lactic acid bacteria. Fermentations involving Sc. pombe consumed all the malic acid, although they reduced the concentrations of higher alcohols, fatty acids and acetic acid. Simultaneous alcoholic and malolactic fermentations reduced malic acid by about 80%, while classical malolactic fermentation reduced it by 100%. Fermentations involving L. thermotolerans produced the highest lactic acid, ester and glycerol concentrations.

Biorefining Potential of Wild-Grown Arundo donax, Cortaderia selloana and Phragmites australis and the Feasibility of White-Rot Fungi-Mediated Pretreatments.

Arundo donax, Cortaderia selloana and Phragmites australis are high-biomass-producing perennial Poalean species that grow abundantly and spontaneously in warm temperate regions, such as in Mediterranean-type climates, like those of Southern Europe, Western United States coastal areas, or in regions of South America, South Africa and Australia. Given their vigorous and spontaneous growth, biomass from the studied grasses often accumulates excessively in unmanaged agro-forestry areas. Nonetheless, this also creates the demand and opportunity for the valorisation of these biomass sources, particularly their cell wall polymers, for biorefining applications. By contrast, a related crop, Miscanthus × giganteus, is a perennial grass that has been extensively studied for lignocellulosic biomass production, as it can grow on low-input agricultural systems in colder climates. In this study Fourier transform mid-infrared spectroscopy (FTIR), high-performance anion-exchange chromatography (HPAEC) and lignin content determinations were used for a comparative compositional characterisation of A. donax, C. selloana and P. australis harvested from the wild, in relation to a trial field-grown M. × giganteus high-yielding genotype. A high-throughput saccharification assay showed relatively high sugar release values from the wild-grown grasses, even with a 0.1M NaOH mild alkali pretreatment. In addition to this alkaline pretreatment, biomass was treated with white-rot fungi (WRF), which preferentially degrade lignin more readily than holocellulose. Three fungal species were used: Ganoderma lucidum, Pleurotus ostreatus and Trametes versicolor. Our results showed that neutral sugar contents are not significantly altered, while some lignin is lost during the pretreatments. Furthermore, sugar release upon enzymatic saccharification was enhanced, and this was dependent on the plant biomass and fungal species used in the treatment. To maximise the potential for lignocellulose valorisation, the liquid fractions from the pretreatments were analysed by high performance liquid chromatography – photodiode array detection – electrospray ionisation tandem mass spectrometry (HPLC-PDA-ESI-MSn). This study is one of the first to report on the composition of WRF-treated grass biomass, while assessing the potential relevance of breakdown products released during the treatments, beyond more traditional sugar-for-energy applications. Ultimately, we expect that our data will help promote the valorisation of unused biomass resources, create economic value, while contributing to the implementation of sustainable biorefining systems.

Predicting weathering indices in soils using FTIR spectra and random forest models

Weathering indices based on the relative proportions of different chemical elements are a useful tool to investigate the degree of weathering of soils. This characterization is missing in West Azerbaijan, northern Iran, and thus the main goals of this work were to assess the suitability of different indices to determine soil weathering, and to predict weathering indices using Fourier Transform Mid-infrared (FTIR) spectroscopy and Random Forest (RF) models. Soil major elemental oxides were determined in 12 profiles by X-ray Fluorescence (XRF), and the degree of chemical weathering was characterized using various indices: Chemical Index of Alteration (CIA), Chemical Index of Weathering (CIW), Mineralogical Index of Alteration (MIA), Weathering Index of Parker (WIP), SiO2/R2O3, Vogt Index (V) and Chemical Proxy of Alteration (CPA). For each index, a RF model based on the FTIR spectra of the samples was optimized and calibrated. The soils showed a weak to moderate degree of weathering, and CIA, CIW, MIA and V were the most appropriate for this characterization. The constructed models showed R2 values of 0.79, 0.75 and 0.71 for MIA, CIA, and CIW, respectively. The important spectral bands for prediction were those related to the presence of smectitic clays, which indicates that weathering is related to the alteration of primary minerals and the neoformation of smectites. The predicted spatial distribution of the weathering indices showed that Typic Haplocalcids and Typic Haploxerepts are at an earlier stage of weathering compared to Haplargids and Haplocambids in the area. The combination of FTIR spectroscopy and RF models is a rapid, efficient, and cost-effective technique to predict weathering indices in large datasets when XRF data are limited.

Advances in Atypical FT-IR Milk Screening: Combining Untargeted Spectra Screening and Cluster Algorithms.

Fourier-transform mid-infrared spectrometry is an attractive technology for screening adulterated liquid milk products. So far, studies on how infrared spectroscopy can be used to screen spectra for atypical milk composition have either used targeted methods to test for specific adulterants, or have used untargeted screening methods that do not reveal in what way the spectra are atypical. In this study, we evaluate the potential of combining untargeted screening methods with cluster algorithms to indicate in what way a spectrum is atypical and, if possible, why. We found that a combination of untargeted screening methods and cluster algorithms can reveal meaningful and generalizable categories of atypical milk spectra. We demonstrate that spectral information (e.g., the compositional milk profile) and meta-data associated with their acquisition (e.g., at what date and which instrument) can be used to understand in what way the milk is atypical and how it can be used to form hypotheses about the underlying causes. Thereby, it was indicated that atypical milk screening can serve as a valuable complementary quality assurance tool in routine FTIR milk analysis.

Chemometric Discrimination of the Varietal Origin of Extra Virgin Olive Oils: Usefulness of 13C Distortionless Enhancement by Polarization Transfer Pulse Sequence and 1H Nuclear Magnetic Resonance Data and Effectiveness of Fusion with Mid-Infrared Spectroscopy Data.

The label authentication of monovarietal extra virgin olives is of great relevance from a socio-economical point of view. This work aims to gain insights into the prediction of the varietal origin of extra virgin olive oil (EVOO) samples obtained from single olive cultivars, French cultivars Olivière, Salonenque, and Tanche and Portuguese cultivars Blanqueta, Carrasquenha, and Galega Vulgar, collected in 2016-2017 and 2017-2018 harvest seasons. To pursue this study, spectroscopic approaches based on one-dimensional nuclear magnetic resonance (1D NMR) spectroscopy, namely, 1H and 13C NMR distortionless enhancement by polarization transfer (DEPT) 45 pulse sequence, and Fourier transform mid-infrared spectroscopy (FT-MIR) are used in combination with partial least square discriminant analysis (PLS1-DA). The results obtained by PLS1-DA models using 1H and 13C NMR DEPT 45 data are compared to those of PLS1-DA models using MIR data. The application of a control chart method allows for the optimization of the interpretation of the PLS1-DA results, and an efficient two-step strategy is proposed to improve the discrimination of the six studied cultivars. Then, NMR and MIR data are combined by either a mid- or high-level data fusion approach to further improve the discrimination. The models are also tested on samples from other cultivars to check their ability to reject varieties that were not considered in the calibration process.

Major characteristics of microplastics in mussels from the Portuguese coast

The present study reports the quantity, shape, colour and chemical properties of microplastics (MP) and MP-like in whole soft tissues of the mussel Mytilus spp. collected in January and February 2019 from four natural banks in the Portuguese coast. Three sites are located in estuarine areas influenced by anthropogenic pressures and freshwater discharges, and one in the coast far from urbanised areas. An alkaline digestion (KOH) of biological tissues was used and a polymeric identification of 20% of the visually sorted particles was achieved using the Fourier-transform mid-infrared spectroscopy (FT-MIR). MP and MP-like concentrations ranged from 0.54 to 3.0 items g−1 without significant differences among the sites. Particle size varied from 36 to 4439 μm, being fibers the most abundant shape (50%) followed by films (22%) and spherules (18%). FT-MIR revealed that 69% of the analysed particles were plastic, being identified six polymers and two polymeric blends, and 32% were cellulose-based materials. Fibers identified in mussel tissues were mainly composed of cotton and viscose (77%). This study emphasizes the importance of the polymer's spectroscopic identification after microscopic observation to recognise MP.

Rapid tannin profiling of tree fodders using untargeted mid-infrared spectroscopy and partial least squares regression

BackgroundThe presence of condensed tannins (CT) in tree fodders entails a series of productive, health and ecological benefits for ruminant nutrition. Current wet analytical methods employed for full CT characterisation are time and resource-consuming, thus limiting its applicability for silvopastoral systems. The development of quick, safe and robust analytical techniques to monitor CT’s full profile is crucial to suitably understand CT variability and biological activity, which would help to develop efficient evidence-based decision-making to maximise CT-derived benefits. The present study investigates the suitability of Fourier-transformed mid-infrared spectroscopy (MIR: 4000–550 cm−1) combined with multivariate analysis to determine CT concentration and structure (mean degree of polymerization—mDP, procyanidins:prodelphidins ratio—PC:PD and cis:trans ratio) in oak, field maple and goat willow foliage, using HCl:Butanol:Acetone:Iron (HBAI) and thiolysis-HPLC as reference methods.ResultsThe MIR spectra obtained were explored firstly using Principal Component Analysis, whereas multivariate calibration models were developed based on partial least-squares regression. MIR showed an excellent prediction capacity for the determination of PC:PD [coefficient of determination for prediction (R2P) = 0.96; ratio of prediction to deviation (RPD) = 5.26, range error ratio (RER) = 14.1] and cis:trans ratio (R2P = 0.95; RPD = 4.24; RER = 13.3); modest for CT quantification (HBAI: R2P = 0.92; RPD = 3.71; RER = 13.1; Thiolysis: R2P = 0.88; RPD = 2.80; RER = 11.5); and weak for mDP (R2P = 0.66; RPD = 1.86; RER = 7.16).ConclusionsMIR combined with chemometrics allowed to characterize the full CT profile of tree foliage rapidly, which would help to assess better plant ecology variability and to improve the nutritional management of ruminant livestock.

Nonlinear interferometer for Fourier-transform mid-infrared gas spectroscopy using near-infrared detection.

Nonlinear interferometers allow for mid-infrared spectroscopy with near-infrared detection using correlated photons. Previous implementations have demonstrated a spectral resolution limited by spectrally selective detection. In our work, we demonstrate mid-infrared transmission spectroscopy in a nonlinear interferometer using single-pixel near-infrared detection and Fourier-transform analysis. A sub-wavenumber spectral resolution allows for rotational-line-resolving spectroscopy of gaseous samples in a spectral bandwidth of over 700 cm-1. We use methane transmission spectra around 3.3 μm wavelength to characterize the spectral resolution, noise limitations and transmission accuracy of our device. The combination of nonlinear interferometry and Fourier-transform analysis paves the way towards performant and efficient mid-infrared spectroscopy with near-infrared detection.

Infrared analysis of ultrasound treated milk systems with different levels of caseins, whey proteins and fat

Low-frequency ultrasound (20 kHz) was applied to model casein (CF) and whey protein (WF) milk systems with varying fat contents of 2%, 4%, and 6% (w/w) under different energy densities from 9.54 to 190.8 J mL−1. The study used Fourier transform mid-infrared spectrometry (FT-MIR) to analyse the modification of milk proteins and fat. The results showed that the fat content and sonication time influenced the intensities of fatty chains and ester groups which are assigned to triacylglycerols and lipid-protein complexes for both CF and WF systems. Acoustic cavitation induced structural changes of proteins due to denaturation/aggregation and disruption of milk fat globules/fat clusters led to new protein–protein and protein-lipid interactions.

Assessing the impacts of climate change and habitat suitability on the distribution and quality of medicinal plant using multiple information integration: Take Gentiana rigescens as an example

Gentiana rigescens is an economically important medicinal plant in the subtropical zone in Asia. However, it is unclear how its distribution responds to climate change in the future. This study aims to simulate the effect of climate change on the distribution of G. rigescens by using the maximum entropy (MaxEnt) model and to evaluate the potential impact of habitat suitability on the quality of medicinal materials vis multiple chemical information. In the study, we investigated the suitable habitat fluctuation ranges of G. rigescens under different future climate scenarios at first. Subsequently, the chemical geographical variation of medicinal materials collected from different suitable areas was evaluated based on high-performance liquid chromatography (HPLC) analysis and Fourier-transform mid-infrared (FT-MIR) spectroscopy. The results showed that the areas of highly suitable habitat for G. rigescens was about 23.17 × 104 km2, and more than 98.88% of highly suitable habitat was distributed in the southwest of China. The survival of G. rigescens may be threatened by global climate change in the future. Under the Representative Concentration Pathways 8.5 (RCP8.5) climate scenario, the change ratio of the highly suitable habitat will be −99.22% in 2070. Meanwhile, the centroids of the highly suitable area showed a westward-shifting trend under future emission trajectories. HPLC analysis showed that medicinal materials from highly suitable areas contained higher concentrations of total iridoid glycoside. Analysis results of FT-MIR data combined with OPLS-DA modeling indicated that the overall chemical composition of samples collected from different suitable habitats were different, and the habitat suitability had a stronger effect on the chemical composition in leaf than that in stem and root. Based on the concentration of iridoids and FT-MIR fingerprint, our research demonstrated there was a certain correlation of habitat suitability with the overall chemical variation of G. rigescens. The highly suitable habitat was better for the cultivation of high-quality medicinal materials. In summary, these findings would improve our understanding of the distribution and quality variation of medicinal Gentiana under future climate change. Meanwhile, the study approach based on multiple information integration could provide a useful case for research of other medicinal plants.

Estimation of Fatty Acids in Intramuscular Fat of Beef by FT-MIR Spectroscopy.

The aim of this research was to estimate the fatty acid (FA) content of intramuscular fat from beef by Fourier transform mid-infrared (FT-MIR) spectroscopy. Four diets were supplemented in 10% linseed (LS) and/or 2% conjugated linoleic acid (CLA): CON (without L or CLA), LS, CLA, and LS+CLA. For each diet, 12 young Holstein bulls were allocated. The spectral response of the beef samples was analyzed applying FT-MIR spectroscopy (from 400 to 4000 cm−1) and predictive models were developed using partial least square regression with cross-validation. The obtained coefficients (R2) for some FA, such as α-linolenic acid with a R2 = 0.96 or n-3 polyunsaturated fatty acids (n-3 PUFA) with R2 = 0.93, demonstrate that FT-MIR spectroscopy is a valid technique to estimate the content of FA. In addition, samples were correctly classified according to the animal diet using discriminant analysis in the region 3000–1000 cm−1. The obtained results suggest that the FT-MIR spectroscopy could be a viable technique for routine use in quality control because it provides fast and sustainable analysis of FA content. Furthermore, this technique allows the rapid estimation of the FA composition, specifically n-3 PUFA and CLA, of nutritional interest in meat. It also allows the classification of meat samples by the animal diet.

Effects of β- and κ-casein, and β-lactoglobulin single and composite genotypes on milk composition and milk coagulation properties of Italian Holsteins assessed by FT-MIR

The present study aimed to investigate the effect of β- and κ- casein, and β-lactoglobulin genotypes on milk composition and milk coagulation properties predicted using Fourier-transform mid-infrared spectroscopy (FT-MIR). The final dataset provided 74,721 observations from 5316 Holstein–Friesian cows reared in 122 herds in the Veneto region. Individual cow milk composition was obtained from Dairy Herd Improvement (DHI) testing between the years 2012 and 2021. Genotypes were analysed through customised bovine genotyping chip. The effect of both separate and combined milk protein genotype on major milk components, SCS, urea, and predicted milk coagulation properties, were investigated applying a linear mixed model. β-lactoglobulin BB resulted in the higher fat content, whereas protein and casein content was mostly enhanced by the k-casein BB haplotype. The urea content was significantly lower in β-lactoglobulin AA and k-casein EE genotypes and SCS was significantly lower in β-casein A1A1, β-lactoglobulin AA, and k-casein BB genotypes. Milk coagulation properties performance was significantly improved by β-casein A1A1, β-LG BB and k-casein BB, both as single variants and as composite genotype. The effect of genotypes on milk composition and milk coagulation properties showed five and four principal hierarchical clusters, respectively. Clustering highlighted unexpected relationships among genotypes in terms of final milk quality, suggesting it could be possible to select favourable genotypes maintaining sufficient diversity. HIGHLIGHTS Milk protein variants have an impact on milk technological properties, but phenotype collection is laborious and expensive. Data collected using mid-infra-red spectroscopy can be used to estimate milk coagulation properties and perform population studies. Milk protein variants revealed unexpected relationships with milk composition and technological traits.

Development of Fast Analytical Method for the Detection and Quantification of Honey Adulteration Using Vibrational Spectroscopy and Chemometrics Tools.

In this study, the Fourier transform mid-infrared (FT-MIR) spectroscopy technique combined with chemometrics methods was used to monitor adulteration of honey with sugar syrup. Spectral data were recorded from a wavenumber region of 4000–600 cm−1, with a spectral resolution of 4 cm−1. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used for qualitative analysis to discriminate between adulterated and nonadulterated honey. For quantitative analysis, we used partial least-squares regression (PLS-R) and the support vector machine (SVM) to develop optimal calibration models. The use of PCA shows that the first two principal components account for 96% of the total variability. PCA and HCA allow classifying the dataset into two groups: adulterated and unadulterated honey. The use of the PLS-R and SVM-R calibration models for the quantification of adulteration shows high-performance capabilities represented by a high value of correlation coefficients R2 greater than 98% and 95% with lower values of root mean square error (RMSE) less than 1.12 and 1.85 using PLS-R and SVM-R, respectively. Our results indicate that FT-MIR spectroscopy combined with chemometrics techniques can be used successfully as a simple, rapid, and nondestructive method for the quantification and discrimination of adulterated honey.