Fourier Transform Mid-infrared Spectra Research Articles

Prediction of detailed blood metabolic profile using milk infrared spectra and machine learning methods in dairy cattle

The adoption of preventive management decisions is crucial to dealing with metabolic impairments in dairy cattle. Various serum metabolites are known to be useful indicators of the health status of cows. In this study, we used milk Fourier-transform mid-infrared (FTIR) spectra and various machine learning (ML) algorithms to develop prediction equations for a panel of 29 blood metabolites, including those related to energy metabolism, liver function/hepatic damage, oxidative stress, inflammation/innate immunity, and minerals. For most traits, the data set comprised observations from 1,204 Holstein-Friesian dairy cows belonging to 5 herds. An exception was represented by β-hydroxybutyrate prediction, which contained observations from 2,701 multibreed cows pertaining to 33 herds. The best predictive model was developed using an automatic ML algorithm that tested various methods, including elastic net, distributed random forest, gradient boosting machine, artificial neural network, and stacking ensemble. These ML predictions were compared with partial least squares regression, the most commonly used method for FTIR prediction of blood traits. Performance of each model was evaluated using 2 cross-validation (CV) scenarios: 5-fold random (CVr) and herd-out (CVh). We also tested the best model's ability to classify values precisely in the 2 extreme tails, namely, the 25th (Q25) and 75th (Q75) percentiles (true-positive prediction scenario). Compared with partial least squares regression, ML algorithms achieved more accurate performance. Specifically, elastic net increased the R2 value from 5% to 75% for CVr and 2% to 139% for CVh, whereas the stacking ensemble increased the R2 value from 4% to 70% for CVr and 4% to 150% for CVh. Considering the best model, with the CVr scenario, good prediction accuracies were obtained for glucose (R2 = 0.81), urea (R2 = 0.73), albumin (R2 = 0.75), total reactive oxygen metabolites (R2 = 0.79), total thiol groups (R2 = 0.76), ceruloplasmin (R2 = 0.74), total proteins (R2 = 0.81), globulins (R2 = 0.87), and Na (R2 = 0.72). Good prediction accuracy in classifying extreme values was achieved for glucose (Q25 = 70.8%, Q75 = 69.9%), albumin (Q25 = 72.3%), total reactive oxygen metabolites (Q25 = 75.1%, Q75 = 74%), thiol groups (Q75 = 70.4%), total proteins (Q25 = 72.4%, Q75 = 77.2.%), globulins (Q25 = 74.8%, Q75 = 81.5%), and haptoglobin (Q75 = 74.4%). In conclusion, our study shows that FTIR spectra can be used to predict blood metabolites with relatively good accuracy, depending on trait, and are a promising tool for large-scale monitoring.

Use of genetic algorithms in the wavelength selection of FT-MIR spectra to classify unifloral honeys from Sardinia

Beekeeping is among the oldest activities in Sardinia (Italy). Among others, here are produced four valuable unifloral honeys appreciated worldwide for their quality and organoleptic properties, i.e., asphodel (Asphodelus microcarpus), eucalyptus (Eucalyptus camaldulensis), strawberry tree (Arbutus unedo L.) and thistle (Galactites tomentosa).The main purpose of this contribution was to assess a botanical classification method by analyzing 125 honeys using Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy.Spectra were used to develop a predictive model by means of linear discriminant analysis (LDA), using different spectral pretreatments techniques. Predictors were selected using principal component analysis (PCA) or genetic algorithms (GA) tools. In particular, GA selected 34 wavelengths in the spectral regions from 1726 to 1543 cm−1, and the application of LDA to this selection provided an accuracy of 93.6% in cross validation and an accuracy of 87.8% in the validation on a test set of honey samples.The results were compared, in terms of pros and cons, with other targeted and non-targeted approaches previously assessed by this research group on the same four unifloral honeys.

Comparison of the genetic characteristics of directly measured and Fourier-transform mid-infrared-predicted bovine milk fatty acids and proteins.

Fourier-transform mid-infrared (FT-MIR) spectroscopy is a high-throughput and inexpensive methodology used to evaluate concentrations of fat and protein in dairy cattle milk samples. The objective of this study was to compare the genetic characteristics of FT-MIR predicted fatty acids and individual milk proteins with those that had been measured directly using gas and liquid chromatography methods. The data used in this study was based on 2,005 milk samples collected from 706 Holstein-Friesian × Jersey animals that were managed in a seasonal, pasture-based dairy system, with milk samples collected across 2 consecutive seasons. Concentrations of fatty acids and protein fractions in milk samples were directly determined by gas chromatography and high-performance liquid chromatography, respectively. Models to predict each directly measured trait based on FT-MIR spectra were developed using partial least squares regression, with spectra from a random selection of half the cows used to train the models, and predictions for the remaining cows used as validation. Variance parameters for each trait and genetic correlations for each pair of measured/predicted traits were estimated from pedigree-based bivariate models using REML procedures. A genome-wide association study was undertaken using imputed whole-genome sequence, and quantitative trait loci (QTL) from directly measured traits were compared with QTL from the corresponding FT-MIR predicted traits. Cross-validation prediction accuracies based on partial least squares for individual and grouped fatty acids ranged from 0.18 to 0.65. Trait prediction accuracies in cross-validation for protein fractions were 0.53, 0.19, and 0.48 for α-casein, β-casein, and κ-casein, 0.31 for α-lactalbumin, 0.68 for β-lactoglobulin, and 0.36 for lactoferrin. Heritability estimates for directly measured traits ranged from 0.07 to 0.55 for fatty acids; and from 0.14 to 0.63 for individual milk proteins. For FT-MIR predicted traits, heritability estimates were mostly higher than for the corresponding measured traits, ranging from 0.14 to 0.46 for fatty acids, and from 0.30 to 0.70 for individual proteins. Genetic correlations between directly measured and FT-MIR predicted protein fractions were consistently above 0.75, with the exceptions of C18:0 and C18:3 cis-3, which had genetic correlations of 0.72 and 0.74, respectively. The GWAS identified trait QTL for fatty acids with likely candidates in the DGAT1, CCDC57, SCD, and GPAT4 genes. Notably, QTL for SCD were largely absent in the FT-MIR predicted traits, and QTL for GPAT4 were absent in directly measured traits. Similarly, for directly measured individual proteins, we identified QTL with likely candidates in the CSN1S1, CSN3, PAEP, and LTF genes, but the QTL for CSN3 and LTF were absent in the FT-MIR predicted traits. Our study indicates that genetic correlations between directly measured and FT-MIR predicted fatty acid and protein fractions are typically high, but that phenotypic variation in these traits may be underpinned by differing genetic architecture.

PSVIII-19 Predict Indigestible Fiber Fraction of Barley Plant Silage by Using non-Destructive Mid-IR vs Near-IR Spectroscopic Techniques

Abstract The objective of this study was to reveal the potential of using Fourier transform mid-infrared (FTIR) and near infrared (NIR) spectroscopy as tools for the determination of indigestible neutral (NDF) fraction (iNDF) of barley plant silage. A total of 48 barley plant silage samples collected from different farms in Western Canada provinces were analyzed for iNDF. Reference values were matched with NIR and FTIR spectra. Spectral data processing (pretreatments) included first derivative (FD), standard normal variate (SNV), multiplicative scattering correction (MSC), second derivative (SD) and orthogonal signal correction (OSC). Prediction equations were obtained from each model using an external validation set. The determination coefficient of external validation (R2P) of iNDF was 0.62 for FTIR, while 0.41 for NIR and the corresponding ratio performance deviation (RPD) were 1.69 and 1.38 in FTIR and NIR, respectively. Results from this research showed the high potential of applying infrared molecular spectroscopy for the examination of forage plant fiber digestibility. More studies are needed to improve the accuracy and performance of FTIR and NIR spectroscopies in predicting the iNDF of barley plant silage samples.

Evaluation of prediction of indigestible fiber fraction (iNDF) of whole-crop barley silage by using non-destructive spectroscopic techniques as a fast-screening method: comparison between FTIR vs. NIR

The objective of this study was to reveal the potential of using Fourier transform mid-infrared (FTIR) and near infrared (NIR) spectroscopy as tools for the determination of indigestible neutral (NDF) fraction (iNDF) of whole-crop barley silage. A total of 48 whole-crop barley silage samples collected from 48 different farms in Western Canada were analyzed for iNDF. Reference values were matched with NIR and FTIR spectra. Spectral data processing (pretreatments) included first derivative, standard normal variate, multiplicative scattering correction, second derivative, and orthogonal signal correction. Prediction equations were obtained from each model using an external validation set. The coefficient of determination for the external validation of iNDF was 0.62 for FTIR and 0.41 for NIR, while the corresponding ratio performance deviation was 1.69 and 1.38 for FTIR and NIR, respectively. Results from this research showed the high potential of applying infrared molecular spectroscopy for the examination of forage plant fiber digestibility. More studies are needed to improve the accuracy and performance of FTIR and NIR spectroscopies in predicting the iNDF of whole-crop barley silage samples.

Genetic analyses of blood β-hydroxybutyrate predicted from milk infrared spectra and its association with longevity and female reproductive traits in Holstein cattle

Ketosis is one of the most prevalent and complex metabolic disorders in high-producing dairy cows and usually detected through analyses of β-hydroxybutyrate (BHB) concentration in blood. Our main objectives were to evaluate genetic parameters for blood BHB predicted based on Fourier-transform mid-infrared spectra from 5 to 305 d in milk, and estimate the genetic relationships of blood BHB with 7 reproduction traits and 6 longevity traits in Holstein cattle. Predicted blood BHB records of 11,609 Holstein cows (after quality control) were collected from 2016 to 2019 and used to derive 4 traits based on parity number, including predicted blood BHB in all parities (BHBp), parity 1 (BHB1), parity 2 (BHB2), and parity 3+ (BHB3). Single- and multitrait repeatability models were used for estimating genetic parameters for the 4 BHB traits. Random regression test-day models implemented via Bayesian inference were used to evaluate the daily genetic feature of BHB variability. In addition, genetic correlations were calculated for the 4 BHB traits with reproduction and longevity traits. The heritability estimates of BHBp, BHB1, BHB2, and BHB3 ranged from 0.100 ± 0.026 (± standard error) to 0.131 ± 0.023. The BHB in parities 1 to 3+ were highly genetically correlated and ranged from 0.788 (BHB1 and BHB2) to 0.911 (BHB1 and BHB3). The daily heritability of BHBp ranged from 0.069 to 0.195, higher for the early and lower for the later lactation periods. A similar trend was observed for BHB1, BHB2, and BHB3. There are low direct genetic correlations between BHBp and selected reproductive performance and longevity traits, which ranged from -0.168 ± 0.019 (BHBp and production life) to 0.157 ± 0.019 (BHBp and age at first calving) for the early lactation stage (5 to 65 d). These direct genetic correlations indicate that cows with higher BHBp (greater likelihood of having ketosis) in blood usually have shorter production life (-0.168 ± 0.019). Cows with higher fertility and postpartum recovery, such as younger age at first calving (0.157 ± 0.019) and shorter interval from calving to first insemination in heifer (0.111 ± 0.006), usually have lower BHB concentration in the blood. Furthermore, the direct genetic correlations change across parity and lactation stage. In general, our results suggest that selection for lower predicted BHB in early lactation could be an efficient strategy for reducing the incidence of ketosis as well as indirectly improving reproductive and longevity performance in Holstein cattle.

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.

Soil C storage following salvage logging and residue management in bark beetle-infested lodgepole pine forests

Bark beetle outbreaks have altered carbon (C) dynamics in lodgepole pine (Pinus contorta var. latifolia Engelm. ex Wats.) forests across western North America. The sensitivity of soil C to post-beetle management operations remains unknown in these forests. We quantified total C stocks in the O horizon and upper mineral soil (0–10 cm) 6–7 years after salvage logging in lodgepole pine forests of northern Colorado and compared biomass retention or removal residue management to uncut beetle-infested forests. We evaluated the quantity and chemical composition of C in physical fractions of the O horizon and upper mineral soil layers to better understand how management impacts the quality of C with implications for soil C persistence. Post-harvest residue retention increased O horizon C storage compared to the removal treatment and uncut stands. Total soil C (O horizon and upper 0–10 cm mineral soil) was an average of 11.4 (±5.6) Mg C ha−1 higher in salvage logged plots compared to uncut forests and the increase was primarily in particulate organic matter. Salvage logging did not alter the chemical composition of the soil C fractions analyzed. The C:N ratios, Fourier-transform mid-infrared spectra, and 13C values analyses each showed that the C composition of the organic and upper mineral soil layers were distinct. This indicates that physical transfer of fresh or decomposed O horizon material may not be the primary source of particulate C, and we suggest that belowground inputs are likely to be an important contributor to these mineral soils. Our finding that logging residue removal did not reduce post-harvest upper mineral soil C stocks has implications for biomass harvesting in these forests, though the long-term impacts of post-bark beetle management on C storage in these ecosystems depend on understory dynamics, tree regeneration and forest recovery that will progress over the course of decades. This work suggests that greater understanding of post-logging organic matter inputs and C dynamics in O horizon and mineral SOM fractions can help inform forest management aimed at sustaining soil C.

Genome-wide association for metabolic clusters in early-lactation Holstein dairy cows

The aim of this study was to detect the genomic region or regions associated with metabolic clusters in early-lactation Holstein cows. This study was carried out in 2 experiments. In experiment I, which was carried out on 105 multiparous Holstein cows, animals were classified through k-means clustering on log-transformed and standardized concentrations of blood glucose, insulin-like growth factor I, free fatty acids, and β-hydroxybutyrate at 14 and 35 d in milk (DIM), into metabolic clusters, either balanced (BAL) or other (OTR). Forty percent of the animals were categorized in the BAL group, and the remainder were categorized as OTR. The cows were genotyped for a total of 777,962 SNP. A genome-wide association study was performed, using a case-control approach through the GEMMA software, accounting for population structure. We found 8 SNP (BTA11, BTA23, and BTAX) associated with the predicted metabolic clusters. In experiment II, carried out on 4,267 second-parity Holstein cows, milk samples collected starting from the first week until 50 DIM were used to determine Fourier-transform mid-infrared (FT-MIR) spectra and subsequently to classify the animals into the same metabolic clusters (BAL vs. OTR). Twenty-eight percent of the animals were categorized in the BAL group, and the remainder were classified in the OTR category. Although daily milk yield was lower in BAL cows, we found no difference in daily fat- and protein-corrected milk yield in cows from the BAL metabolic cluster compared with those in the OTR metabolic cluster. In the next step, a single-step genomic BLUP was used to identify the genomic region(s) associated with the predicted metabolic clusters. The results revealed that prediction of metabolic clusters is a highly polygenic trait regulated by many small-sized effects. The region of 36,258 to 36,295 kb on BTA27 was the highly associated region for the predicted metabolic clusters, with the closest genes to this region (ANK1 and miR-486) being related to hematopoiesis, erythropoiesis, and mammary gland development. The heritability for metabolic clustering was 0.17 (SD 0.03), indicating that the use of FT-MIR spectra in milk to predict metabolic clusters in early-lactation across a large number of cows has satisfactory potential to be included in genetic selection programs for modern dairy cows.

Only few benefits from propylene glycol drench in early lactation for cows identified as physiologically imbalanced based on milk spectra analyses

The main objective of this study was to test the efficiency of a management system combining metabolic clustering of cows based on Fourier-transform mid-infrared (FT-MIR) spectra of milk and targeted treatment of metabolically imbalanced cows with propylene glycol drench. We hypothesized that cows identified in a metabolically imbalanced status during early lactation were associated with subsequent impaired health, reproduction, and production, and that treatment with propylene glycol treatment would improve health, reproduction, and production relatively more in these cows than in control cows. We completed a prospective, randomized controlled trial with 356 early-lactation cows in 2 private dairy herds in Denmark from December 2017 to April 2018. Milk samples of cows were collected before treatment, from 4 to 9 d in milk, and after treatment, from 22 to 27 d in milk. Milk samples were analyzed using FT-MIR spectroscopy. We also measured 4 milk metabolites (β-hydroxybutyrate, isocitrate, malate, and glutamate) and fat and protein contents. Based on FT-MIR spectra and cluster analyses, cows were clustered into groups of metabolically imbalanced and healthy cows. Within each group, cows were allocated randomly to treatment with propylene glycol (500 mL for 5 d) or no treatment. We analyzed the effect of the treatment on cow-level variables: metabolic cluster, milk metabolites, fat and protein contents, and fat-to-protein ratio at a milk sampling after the treatment. Furthermore, we analyzed daily milk yield, calving to first service interval, and disease occurrence. Results showed only a few effects of propylene glycol treatment and few interactions between treatment and metabolic clusters. We found no significant main effects of propylene glycol treatment in any of these analyses. A negative effect of the imbalanced metabolic cluster was found for the outcome of calving to first service interval for multiparous cows. In conclusion, we found a longer calving to first service interval in metabolically imbalanced cows, but we were not able to demonstrate overall benefits from the applied detection of cows in imbalanced metabolic status in early lactation and follow-up by treatment with propylene glycol.

Origin identification of Panax notoginseng by multi-sensor information fusion strategy of infrared spectra combined with random forest.

Traditional Chinese medicine Panax notoginseng is a valuable geo-authentic herbal material. The difference of growth environment in different producing areas has significant influence on the quality of traditional Chinese medicine, and origin identification is an important part of the quality assessment of P.notoginseng. In this study, Fourier transform mid-infrared (FT-MIR) and near infrared (NIR) sensor technologies combined with single spectra analysis and multi-sensor information fusion strategy (low-, mid- and high-level) for the origin identification of 210 P.notoginseng samples from five cities in Yunnan Province, China. FT-MIR spectra were considered to play a greater role in data analysis than NIR spectra. Random forest (RF) was used to establish classification models. The result of the random forest Boruta (RF-Bo) model and the random forest variable selection (RF-Vs) model based on high-level multi-sensor information fusion strategy was satisfactory. In addition, the RF-Bo model based on high-level multi-sensor information fusion strategy was faster and simpler in data analysis and the accuracy was 95.6%.

Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris polyphylla var. yunnanensis.

Origin traceability is important for controlling the effect of Chinese medicinal materials and Chinese patent medicines. Paris polyphylla var. yunnanensis is widely distributed and well-known all over the world. In our study, two spectroscopic techniques (Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR)) were applied for the geographical origin traceability of 196 wild P. yunnanensis samples combined with low-, mid-, and high-level data fusion strategies. Partial least squares discriminant analysis (PLS-DA) and random forest (RF) were used to establish classification models. Feature variables extraction (principal component analysis—PCA) and important variables selection models (recursive feature elimination and Boruta) were applied for geographical origin traceability, while the classification ability of models with the former model is better than with the latter. FT-MIR spectra are considered to contribute more than NIR spectra. Besides, the result of high-level data fusion based on principal components (PCs) feature variables extraction is satisfactory with an accuracy of 100%. Hence, data fusion of FT-MIR and NIR signals can effectively identify the geographical origin of wild P. yunnanensis.

Strategies for noise reduction and standardization of milk mid-infrared spectra from dairy cattle

The use of Fourier-transform mid-infrared (FTIR) spectroscopy is of interest to the dairy industry worldwide for predicting milk composition and other novel traits that are difficult or expensive to measure directly. Although there are many valuable applications for FTIR spectra, noise from differences in spectral responses between instruments is problematic because it reduces prediction accuracy if ignored. The purpose of this study was to develop strategies to reduce the impact of noise and to compare methods for standardizing FTIR spectra in order to reduce between-instrument variability in multiple-instrument networks. Noise levels in bands of the infrared spectrum caused by the water content of milk were characterized, and a method for identifying and removing outliers was developed. Two standardization methods were assessed and compared: piecewise direct standardization (PDS), which related spectra on a primary instrument to spectra on 5 other (secondary) instruments using identical milk-based reference samples (n = 918) analyzed across the 6 instruments; and retroactive percentile standardization (RPS), whereby percentiles of observed spectra from routine milk test samples (n = 2,044,094) were used to map and exploit primary- and secondary-instrument relationships. Different applications of each method were studied to determine the optimal way to implement each method across time. Industry-standard predictions of milk components from 2,044,094 spectra records were regressed against predictions from spectra before and after standardization using PDS or RPS. The PDS approach resulted in an overall decrease in root mean square error between industry-standard predictions and predictions from spectra from 0.190 to 0.071 g/100 mL for fat, from 0.129 to 0.055 g/100 mL for protein, and from 0.143 to 0.088 g/100 mL for lactose. Reductions in prediction error for RPS were similar but less consistent than those for PDS across time, but similar reductions were achieved when PDS coefficients were updated monthly and separate primary instruments were assigned for the North and South Islands of New Zealand. We demonstrated that the PDS approach is the most consistent method to reduce prediction errors across time. We also showed that the RPS approach is sensitive to shifts in milk composition but can be used to reduce prediction errors, provided that secondary-instrument spectra are standardized to a primary instrument with samples of broadly equivalent milk composition. Appropriate implementation of either of these approaches will improve the quality of predictions based on FTIR spectra for various downstream applications.

Prediction of metabolic clusters in early-lactation dairy cows using models based on milk biomarkers

The aim of this study was to describe metabolism of early-lactation dairy cows by clustering cows based on glucose, insulin-like growth factor I (IGF-I), free fatty acid, and β-hydroxybutyrate (BHB) using the k-means method. Predictive models for metabolic clusters were created and validated using 3 sets of milk biomarkers (milk metabolites and enzymes, glycans on the immunogamma globulin fraction of milk, and Fourier-transform mid-infrared spectra of milk). Metabolic clusters are used to identify dairy cows with a balanced or imbalanced metabolic profile. Around 14 and 35 d in milk, serum or plasma concentrations of BHB, free fatty acids, glucose, and IGF-I were determined. Cows with a favorable metabolic profile were grouped together in what was referred to as the "balanced" group (n = 43) and were compared with cows in what was referred to as the "other balanced" group (n = 64). Cows with an unfavorable metabolic profile were grouped in what was referred to as the "imbalanced" group (n = 19) and compared with cows in what was referred to as the "other imbalanced" group (n = 88). Glucose and IGF-I were higher in balanced compared with other balanced cows. Free fatty acids and BHB were lower in balanced compared with other balanced cows. Glucose and IGF-I were lower in imbalanced compared with other imbalanced cows. Free fatty acids and BHB were higher in imbalanced cows. Metabolic clusters were related to production parameters. There was a trend for a higher daily increase in fat- and protein-corrected milk yield in balanced cows, whereas that of imbalanced cows was higher. Dry matter intake and the daily increase in dry matter intake were higher in balanced cows and lower in imbalanced cows. Energy balance was continuously higher in balanced cows and lower in imbalanced cows. Weekly or twice-weekly milk samples were taken and milk metabolites and enzymes (milk glucose, glucose-6-phosphate, BHB, lactate dehydrogenase, N-acetyl-β-d-glucosaminidase, isocitrate), immunogamma globulin glycans (19 peaks), and Fourier-transform mid-infrared spectra (1,060 wavelengths reduced to 15 principal components) were determined. Milk biomarkers with or without additional cow information (days in milk, parity, milk yield features) were used to create predictive models for the metabolic clusters. Accuracy for prediction of balanced (80%) and imbalanced (88%) cows was highest using milk metabolites and enzymes combined with days in milk and parity. The results and models of the present study are part of the GplusE project and identify novel milk-based phenotypes that may be used as predictors for metabolic and performance traits in early-lactation dairy cows.

Short communication: Development of an equation for estimating methane emissions of dairy cows from milk Fourier transform mid-infrared spectra by using reference data obtained exclusively from respiration chambers

Evaluation and mitigation of enteric methane (CH4) emissions from ruminant livestock, in particular from dairy cows, have acquired global importance for sustainable, climate-smart cattle production. Based on CH4 reference measurements obtained with the SF6 tracer technique to determine ruminal CH4 production, a current equation permits evaluation of individual daily CH4 emissions of dairy cows based on milk Fourier transform mid-infrared (FT-MIR) spectra. However, the respiration chamber (RC) technique is considered to be more accurate than SF6 to measure CH4 production from cattle. This study aimed to develop an equation that allows estimating CH4 emissions of lactating cows recorded in an RC from corresponding milk FT-MIR spectra and to challenge its robustness and relevance through validation processes and its application on a milk spectral database. This would permit confirming the conclusions drawn with the existing equation based on SF6 reference measurements regarding the potential to estimate daily CH4 emissions of dairy cows from milk FT-MIR spectra. A total of 584 RC reference CH4 measurements (mean ± standard deviation of 400 ± 72 g of CH4/d) and corresponding standardized milk mid-infrared spectra were obtained from 148 individual lactating cows between 7 and 321 d in milk in 5 European countries (Germany, Switzerland, Denmark, France, and Northern Ireland). The developed equation based on RC measurements showed calibration and cross-validation coefficients of determination of 0.65 and 0.57, respectively, which is lower than those obtained earlier by the equation based on 532 SF6 measurements (0.74 and 0.70, respectively). This means that the RC-based model is unable to explain the variability observed in the corresponding reference data as well as the SF6-based model. The standard errors of calibration and cross-validation were lower for the RC model (43 and 47 g/d vs. 66 and 70 g/d for the SF6 version, respectively), indicating that the model based on RC data was closer to actual values. The root mean squared error (RMSE) of calibration of 42 g/d represents only 10% of the overall daily CH4 production, which is 23 g/d lower than the RMSE for the SF6-based equation. During the external validation step an RMSE of 62 g/d was observed. When the RC equation was applied to a standardized spectral database of milk recordings collected in the Walloon region of Belgium between January 2012 and December 2017 (1,515,137 spectra from 132,658 lactating cows in 1,176 different herds), an average ± standard deviation of 446 ± 51 g of CH4/d was estimated, which is consistent with the range of the values measured using both RC and SF6 techniques. This study confirmed that milk FT-MIR spectra could be used as a potential proxy to estimate daily CH4 emissions from dairy cows provided that the variability to predict is covered by the model.

Mid-Infrared Spectroscopic Assessment of Plasticity Characteristics of Clay Soils

This work presents a method to determine the plasticity of clay soils using Fourier transform mid-infrared (FT-MIR) spectroscopy. Samples of mono- and polymineral soils of varying water contents are studied. The FT-MIR results are compared with the results obtained from standard Russian and international methods for the plasticity range. The correlation between the consistency of clay soils, when displaying their plastic properties, and the position of the Si–O stretching band in the FT-MIR spectra is established. The possibility of, and interest in, determining the plasticity characteristics of clay soils using mid-infrared spectroscopy is demonstrated: it yields effectively higher precision results compared to standard test methods. It is shown that the method of IR spectroscopy allows the fixing of the start and the completion of the series of “phase transitions” of the soil in the plastic and liquid state. The significant effect of the concentration of non-clay minerals on the Si–O ν line is that a moisture content curve is noticed, which may help to predict the clay content of the soil without undertaking XRD analysis.

Predicting methane emissions of lactating Danish Holstein cows using Fourier transform mid-infrared spectroscopy of milk

Enteric methane (CH4), a potent greenhouse gas, is among the main targets of mitigation practices for the dairy industry. A measurement technique that is rapid, inexpensive, easy to use, and applicable at the population level is desired to estimate CH4 emission from dairy cows. In the present study, feasibility of milk Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for CH4:CO2 ratio and CH4 production (L/d) is explained. The partial least squares regression method was used to develop the prediction models. The models were validated using different random test sets, which are independent from the training set by leaving out records of 20% cows for validation and keeping records of 80% of cows for training the model. The data set consisted of 3,623 records from 500 Danish Holstein cows from both experimental and commercial farms. For both CH4:CO2 ratio and CH4 production, low prediction accuracies were found when models were obtained using FT-IR spectra. Validated coefficient of determination (R2Val) = 0.21 with validated model error root mean squared error of prediction (RMSEP) = 0.0114 L/d for CH4:CO2 ratio, and R2Val = 0.13 with RMSEP = 111 L/d for CH4 production. The important spectral wavenumbers selected using the recursive partial least squares method represented major milk components fat, protein, and lactose regions of the spectra. When fat and protein predicted by FT-IR were used instead of full spectra, a low R2Val of 0.07 was obtained for both CH4:CO2 ratio and CH4 production prediction. Other spectral wavenumbers related to lactose (carbohydrate) or additional wavenumbers related to fat or protein (amide II) are providing additional variation when using the full spectral profile. For CH4:CO2 ratio prediction, integration of FT-IR with other factors such as milk yield, herd, and lactation stage showed improvement in the prediction accuracy. However, overall prediction accuracy remained modest; R2Val increased to 0.31 with RMSEP = 0.0105. For prediction of CH4 production, the added value of FT-IR along with the aforementioned traits was marginal. These results indicated that for CH4 production prediction, FT-IR profiles reflect primarily information related to milk yield, herd, and lactation stage rather than individual milk fatty acids related to CH4 emission. Thus, it is not feasible to predict CH4 emission based on FT-IR spectra alone.

An attempt at predicting blood β-hydroxybutyrate from Fourier-transform mid-infrared spectra of milk using multivariate mixed models in Polish dairy cattle.

Fourier transform mid-infrared (FT-MIR) spectra of milk are commonly used for phenotyping of traits of interest through links developed between the traits and milk FT-MIR spectra. Predicted traits are then used in genetic analysis for ultimate phenotypic prediction using a single-trait mixed model that account for cows' circumstances at a given test day. Here, this approach is referred to as indirect prediction (IP). Alternatively, FT-MIR spectral variable can be kept multivariate in the form of factor scores in REML and BLUP analyses. These BLUP predictions, including phenotype (predicted factor scores), were converted to single-trait through calibration outputs; this method is referred to as direct prediction (DP). The main aim of this study was to verify whether mixed modeling of milk spectra in the form of factors scores (DP) gives better prediction of blood β-hydroxybutyrate (BHB) than the univariate approach (IP). Models to predict blood BHB from milk spectra were also developed. Two data sets that contained milk FT-MIR spectra and other information on Polish dairy cattle were used in this study. Data set 1 (n = 826) also contained BHB measured in blood samples, whereas data set 2 (n = 158,028) did not contain measured blood values. Part of data set 1 was used to calibrate a prediction model (n = 496) and the remaining part of data set 1 (n = 330) was used to validate the calibration models, as well as to evaluate the DP and IP approaches. Dimensions of FT-MIR spectra in data set 2 were reduced either into 5 or 10 factor scores (DP) or into a single trait (IP) with calibration outputs. The REML estimates for these factor scores were found using WOMBAT. The BLUP values and predicted BHB for observations in the validation set were computed using the REML estimates. Blood BHB predicted from milk FT-MIR spectra by both approaches were regressed on reference blood BHB that had not been used in the model development. Coefficients of determination in cross-validation for untransformed blood BHB were from 0.21 to 0.32, whereas that for the log-transformed BHB were from 0.31 to 0.38. The corresponding estimates in validation were from 0.29 to 0.37 and 0.21 to 0.43, respectively, for untransformed and logarithmic BHB. Contrary to expectation, slightly better predictions of BHB were found when univariate variance structure was used (IP) than when multivariate covariance structures were used (DP). Conclusive remarks on the importance of keeping spectral data in multivariate form for prediction of phenotypes may be found in data sets where the trait of interest has strong relationships with spectral variables.

Geographical traceability of wild Boletus edulis based on data fusion of FT-MIR and ICP-AES coupled with data mining methods (SVM)

In this work, the data fusion strategy of Fourier transform mid infrared (FT-MIR) spectroscopy and inductively coupled plasma-atomic emission spectrometry (ICP-AES) was used in combination with Support Vector Machine (SVM) to determine the geographic origin of Boletus edulis collected from nine regions of Yunnan Province in China. Firstly, competitive adaptive reweighted sampling (CARS) was used for selecting an optimal combination of key wavenumbers of second derivative FT-MIR spectra, and thirteen elements were sorted with variable importance in projection (VIP) scores. Secondly, thirteen subsets of multi-elements with the best VIP score were generated and each subset was used to fuse with FT-MIR. Finally, the classification models were established by SVM, and the combination of parameter C and γ (gamma) of SVM models was calculated by the approaches of grid search (GS) and genetic algorithm (GA). The results showed that both GS-SVM and GA-SVM models achieved good performances based on the #9 subset and the prediction accuracy in calibration and validation sets of the two models were 81.40% and 90.91%, correspondingly. In conclusion, it indicated that the data fusion strategy of FT-MIR and ICP-AES coupled with the algorithm of SVM can be used as a reliable tool for accurate identification of B. edulis, and it can provide a useful way of thinking for the quality control of edible mushrooms.

Study on FT-MIR spectra of ghee (anhydrous milk fat)

PurposeGhee, anhydrous milk fat, is chemically highly complex in nature. The authentication and characterization of edible fats and oils by routine chemical methods are highly laborious and time consuming. Fourier transform-mid-infrared (FT-MIR) spectroscopy has emerged as a predominant analytical tool in the study of edible fats/oils. However, sufficient attention has not been paid so far to spectral characterization of milk fat obtained from cow and buffalo milk. The purpose of this paper is to fill this void.Design/methodology/approachGhee samples were prepared from cow and buffalo milk by the direct cream method. From each type of milk (cow and buffalo), 35 samples of ghee were prepared; thus, in total, 70 samples of ghee were obtained for the study. For assigning absorption bands in the IR spectrum, spectra of cow and buffalo ghee samples were acquired in the MIR region (4,000-650 cm−1).FindingsIn FT-MIR spectra of ghee, 14 peaks were obtained at different positions and with varying intensities. They were at 3,005, 2,922, 2,853, 1,744, 1,466, 1,418, 1,377, 1,236, 1,161, 1,114, 1,098, 966, 870 and 721 cm−1 for cow and buffalo ghee with almost equal intensity of absorption.Practical implicationsThe finding of this study will be useful for characterization and authentication of ghee.Originality/valueApplication of IR spectral bands of ghee in the MIR region using a FT-infrared spectrometer to monitor the quality of ghee is suggested.