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ABSTRACT: Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1) to assess if milk MIRS data could improve DMI predictions of Canadian Holstein cows using artificial neural networks (ANN); (2) to investigate the ability of different ANN architectures to predict unobserved DMI; and (3) to validate the robustness of developed prediction models. A total of 7,398 milk samples from 509 dairy cows distributed over Canada, Denmark, and the United States were analyzed. Data from Denmark and the United States were used to increase the training data size and variability to improve the generalization of the prediction models over the lactation. For each milk spectra record, the corresponding weekly average DMI (kg/d), test-day milk yield (MY, kg/d), fat yield (FY, g/d), and protein yield (PY, g/d), metabolic body weight (MBW), age at calving, year of calving, season of calving, days in milk, lactation number, country, and herd were available. The weekly average DMI was predicted with various ANN architectures using 7 predictor sets, which were created by different combinations MY, FY, PY, MBW, and MIRS data. All predictor sets also included age of calving and days in milk. In addition, the classification effects of season of calving, country, and lactation number were included in all models. The explored ANN architectures consisted of 3 training algorithms (Bayesian regularization, Levenberg-Marquardt, and scaled conjugate gradient), 2 types of activation functions (hyperbolic tangent and linear), and from 1 to 10 neurons in hidden layers). In addition, partial least squares regression was also applied to predict the DMI. Models were compared using cross-validation based on leaving out 10% of records (validation A) and leaving out 10% of cows (validation B). Superior fitting statistics of models comprising MIRS information compared with the models fitting milk, fat and protein yields suggest that other unknown milk components may help explain variation in weekly average DMI. For instance, using MY, FY, PY, and MBW as predictor variables produced a predictive accuracy (r) ranging from 0.510 to 0.652 across ANN models and validation sets. Using MIRS together with MY, FY, PY, and MBW as predictors resulted in improved fitting (r = 0.679–0.777). Including MIRS data improved the weekly average DMI prediction of Canadian Holstein cows, but it seems that MIRS predicts DMI mostly through its association with milk production traits and its utility to estimate a measure of feed efficiency that accounts for the level of production, such as residual feed intake, might be limited and needs further investigation. The better predictive ability of nonlinear ANN compared with linear ANN and partial least squares regression indicated possible nonlinear relationships between weekly average DMI and the predictor variables. In general, ANN using Bayesian regularization and scaled conjugate gradient training algorithms yielded slightly better weekly average DMI predictions compared with ANN using the Levenberg-Marquardt training algorithm.

Soil organic carbon (SOC) is an acknowledged indicator for land degradation, but conventional determination of SOC remains tedious, especially regarding SOC stock (in kg C m−2 for a given depth layer), which is the product of SOC concentration (g C kg−1) by volumetric mass (kg dm−3). Diffuse reflectance infrared spectroscopy (DRIS) is a time‐ and cost‐effective approach, which uses calibrations for making predictions. The aim of this paper is to propose an overview of DRIS uses for estimating SOC, thus land degradation. Indeed, many papers have demonstrated the precision of DRIS for quantifying SOC concentration, at different scales. Current development of large soil calibration databases and improvements in spectral data analysis pave the way for ever‐wider use of DRIS, which should help solving the soil data crisis, regarding SOC especially. The increasing availability of portable spectrometers allows SOC quantification in the field, which seems particularly promising; but large calibration databases made of soil spectra acquired in the field are difficult to build, while large collections of analyzed soil samples (air‐dried, 2‐mm sieved) already exist. Some recent studies indicate that DRIS can also be used for predicting SOC stock, even from sieved samples, which represents an efficient option because determining the volumetric mass is particularly tedious and an obstacle for exactly specifying the role of soils in the global carbon cycle. In short, DRIS has strong potential for supporting better evaluation of soil and land degradation, and the availability of spectrometers at increasingly affordable prices reinforces this potential. [ABSTRACT FROM AUTHOR]

The objective of this study was to compare mid-infrared reflectance spectroscopy (MIRS) analysis of milk and near-infrared reflectance spectroscopy (NIRS) analysis of feces with regard to their ability to predict the dry matter intake (DMI) of lactating grazing dairy cows. A data set comprising 1,074 records of DMI from 457 cows was available for analysis. Linear regression and partial least squares regression were used to develop the equations using the following variables: (1) milk yield (MY), fat percentage, protein percentage, body weight (BW), stage of lactation (SOL), and parity (benchmark equation); (2) MIRS wavelengths; (3) MIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (4) NIRS wavelengths; (5) NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (6) MIRS and NIRS wavelengths; and (7) MIRS wavelengths, NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity. The equations were validated both within herd using animals from similar experiments and across herds using animals from independent experiments. The accuracy of equations was greater for within-herd validation compared with across-herds validation. Across-herds validation was deemed the more suitable method to assess equations for robustness and real-world application. The benchmark equation was more accurate [coefficient of determination (R2) = 0.60; root mean squared error (RMSE) = 1.68 kg] than MIRS alone (R2 = 0.30; RMSE = 2.23 kg) or NIRS alone (R2 = 0.16; RMSE = 2.43 kg). The combination of the benchmark equation with MIRS (R2 = 0.64; RMSE = 1.59 kg) resulted in slightly superior fitting statistics compared with the benchmark equation alone. The combination of the benchmark equation with NIRS (R2 = 0.58; RMSE = 1.71 kg) did not result in a more accurate prediction equation than the benchmark equation. The combination of MIRS and NIRS wavelengths resulted in superior fitting statistics compared with either method alone (R2 = 0.36; RMSE = 2.15 kg). The combination of the benchmark equation and MIRS and NIRS wavelengths resulted in the most accurate equation (R2 = 0.68; RMSE = 1.52 kg). A further analysis demonstrated that Holstein-Friesian cows could predict the DMI of Jersey × Holstein-Friesian crossbred cows using both MIRS and NIRS. Similarly, the Jersey × Holstein-Friesian animals could predict the DMI of Holstein-Friesian cows using both MIRS and NIRS. The equations developed in this study have the capacity to predict DMI of grazing dairy cows. From a practicality perspective, MIRS in combination with variables in the benchmark equation is the most suitable equation because MIRS is currently used on all milk-recorded milk samples from dairy cows. [ABSTRACT FROM AUTHOR]

Abstract Rapid analysis of oil-contaminated soils is important to facilitate risk assessment and remediation decision-making process. This study reports on the potential of a handheld mid-infrared (MIR) spectrometer for the prediction of total petroleum hydrocarbons (TPH), including aliphatic (alkanes) and polycyclic aromatic hydrocarbons (PAH) in limited number of fresh soil samples. Partial least squares regression (PLSR) and random forest (RF) modelling techniques were compared for the prediction of alkanes, PAH, and TPH concentrations in soil samples (n = 85) collected from three contaminated sites located in the Niger Delta, Southern Nigeria. Results revealed that prediction of RF models outperformed the PLSR with coefficient of determination (R2) values of 0.80, 0.79 and 0.72, residual prediction deviation (RPD) values of 2.35, 1.96, and 2.72, and root mean square error of prediction (RMSEP) values of 63.80, 83.0 and 65.88 mg kg−1 for TPH, alkanes, and PAH, respectively. Considering the limited dataset used in the independent validation (18 samples), accurate predictions were achieved with RF for PAH and TPH, while the prediction for alkanes was less accurate. Therefore, results suggest that RF calibration models can be used successfully to predict TPH and PAH using handheld MIR spectrophotometer under field measurement conditions. Graphical abstract Unlabelled Image Highlights • Handheld MIR spectrometry and chemometrics as a rapid measurement of hydrocarbons • Random forest (RF) outperformed PLSR models for predicting TPH, PAH, and alkanes in soils. • RF-MIR method can be used for predicting hydrocarbons under field conditions. [ABSTRACT FROM AUTHOR]

The aims of this work were (1) to develop prediction equations from mid-infrared spectroscopy (MIRS) to establish a detailed fatty acid (FA) composition of milk; (2) to propose a milk FA index, utilizing MIRS-developed equations, in which the precision of the FA-prediction equations is taken into account to increase the value of milk; and (3) to show application examples. A total of 651 bulk cow milk samples were collected from 245 commercial farms in northwest Italy. The results of the 651 gas chromatography analyses were used to establish (421 samples) and to validate (230 samples) the outcomes of the FA composition prediction that had been obtained by MIRS. A class-based approach, in which the obtained MIRS equations were used, was proposed to define a milk classification. The method provides a numerical index [milk FA index (MFAI)] that allows a premium price to be quantified to increase the value of a favorable FA profile of milk. Ten FA were selected to calculate MFAI, according to their relevance for human health and potential cheese sensory properties, and animal welfare and environmental sustainability were also considered. These factors were selected as dimensions of MFAI. A statistical analysis and expert judgment aggregation were performed on the selected FA by weighting the FA and normalizing the dimensions to reduce redundancy. A class approach was applied, using the precision of the MIRS equations to establish the classes. The median FA concentration of the data set was set as a reference value of class 0. The width, number, and limits of classes above and below the median were calculated using the 95% confidence level of the standard error of prediction, corrected with the bias of each FA. A progressive number and a positive or negative sign were assigned to each FA class above or below the median according to their role in the above mentioned dimensions. The sum of the numbers of each class, associated with its sign for each FA, was used to generate MFAI. The MFAI was applied to dairy farms characterized by different feeding strategies, all of which deliver milk to a commercial dairy plant. The MFAI values ranged from 0.7 to 4.2, and large variations, which depended on the cows' diet and forage quality, were observed for each feeding system. The proposed method has been found to be flexible and adaptable to several contexts on both intensive and extensive dairy farms. [ABSTRACT FROM AUTHOR]

• 96 Tunisian soil samples were used to calibrate and validate SIC prediction models. • MIR absorption peak-based LR and full spectra-based PLSR models were used. • Both types of model were tested on 2178 French soil samples for SIC prediction. • Peak at 2510 cm−1 on Test soils samples was used to select suitable model. • SIC was accurately predicted by a LR and PLSR coupling. Mid-Infrared reflectance spectroscopy (MIRS, 4000–400 cm−1) is being considered to provide accurate estimations of soil inorganic carbon (SIC) contents, based on prediction models when the test dataset is well represented by the calibration set, with similar SIC range and distribution and pedological context. This work addresses the case where the test dataset, here originating from France, is poorly represented by the calibration set, here originating from Tunisia, with different SIC distributions and pedological contexts. It aimed to demonstrate the usefulness of 1) classifying test samples according to SIC level based on the height of the carbonate absorbance peak at 2510 cm−1, and then 2) selecting a suitable prediction model according to SIC level. Two regression methods were tested: Linear Regression using the height of the carbonate peak at 2510 cm−1, called Peak-LR model; and Partial Least Squares Regression using the entire MIR spectrum, called Full-PLSR model. First, our results showed that Full-PLSR was 1) more accurate than Peak-LR on the Tunisian validation set (R2 val = 0.99 vs. 0.86 and RMSE val = 3.0 vs. 9.7 g kg−1, respectively), but 2) less accurate than Peak-LR when applied on the French dataset (R2 test = 0.70 vs. 0.91 and RMSE test = 13.7 vs. 4.9 g kg−1, respectively). Secondly, on the French dataset, predictions on SIC-poor samples tended to be more accurate using Peak-LR , while predictions on SIC-rich samples tended to be more accurate using Full-PLSR. Thirdly, the height of the carbonate absorbance peak at 2510 cm−1 might be used to discriminate SIC-poor and SIC-rich test samples (<5 vs. > 5 g kg−1): when this height was > 0, Full-PLSR was applied; otherwise Peak-LR was applied. Coupling Peak-LR and Full-PLSR models depending on the carbonate peak yielded the best predictions on the French dataset (R2 test = 0.95 and RMSE test = 3.7 g kg−1). This study underlined the interest of using a carbonate peak to select suitable regression approach for predicting SIC content in a database with different distribution than the calibration database. [ABSTRACT FROM AUTHOR]

As a representative of the third-generation semiconductors, ZnO has great development potential in the fields of optics and electricity, so the measurement on its electrical properties is quite important. Here, we try to extract the carrier concentration and mobility of ZnO by mid-infrared reflectance spectroscopy. Considering that the longitudinal optical phonon-plasmon coupling (LOPC) mode formed by the coupling of ZnO optical phonons and free carriers depends on carrier concentration and mobility, we conduct a test on the MIR spectra (400-1600 cm−1) of ZnO crystals with different carrier concentration (1018-1015 cm−3). By fitting the strong reflection peak shape corresponding to phonon-polaritons formed by the coupling of its LOPC and infrared light through m-CDF model, the carrier concentration and mobility of these samples are obtained, which are consistent with those got from the Hall-effect measurement. • Extracted carrier concentration by fitting LOPC+ mode of the MIR spectrum of ZnO with different doping concentrations. • Determined the relationship between the LO and TO phonon damping of semiconductor with the change of doping concentration. • Proposed that combining LO phonon frequency fluctuation and considering crystal anisotropy can further fit the LOPC peak. [ABSTRACT FROM AUTHOR]

• A French MIRS soil database was used for SOC and SIC predictions. • As expected, SOC and SIC of French samples were successfully predicted. • SIC of Tunisian samples was also predicted successfully. • Local calibration significantly improved SOC prediction of Tunisian samples. • Ln transformation of SOC calibration data provided better accuracy than local model. Mid-infrared reflectance spectroscopy (MIRS, 4000–400 cm−1) is being considered to provide accurate estimations of soil properties, including soil organic carbon (SOC) and soil inorganic carbon (SIC) contents. This approach has mainly been demonstrated by using datasets originating from the same area A , with similar geopedological conditions, to build, validate and test prediction models. The objective of this study was to analyse how MIRS performs when applied to predict SOC and SIC contents, from a calibration database collected over a region A , to predict over a region B, where A and B have no common area and different soil and climate conditions. This study used a French MIRS soil dataset including 2178 topsoil samples to calibrate SIC and SOC prediction models with partial least squares regression (PLSR), and a Tunisian MIRS topsoil dataset including 96 soil samples to test them. Our results showed that when using the French MIRS soil database, i) the SOC and SIC of French validation samples were successfully predicted using global models (R2 val = 0.88 and 0.98, respectively), ii) the SIC of Tunisian samples was also predicted successfully both using a global model and using a selection of spectral neighbours from the French calibration database (R2 test of 0.96 for both), iii) the SOC of Tunisian samples was predicted moderately well by global model (R2 test of 0.64) and a transformation by natural logarithm of the calibration SOC values significantly improved the SOC prediction of Tunisian samples (R2 test of 0.97), and iv) a transformation by natural logarithm of SOC values provided more benefit than a selection of spectral neighbours from the French calibration database for predicting Tunisian SOC values. Therefore, in the future, MIRS might replace conventional physico-chemical analysis techniques, or at least be considered as an alternative technique, especially when optimally exhaustive calibration databases will become available. [ABSTRACT FROM AUTHOR]