Mid-infrared Spectroscopy Spectra Research Articles

Application of mid-infrared spectroscopy to the prediction and specification of pesticide sorption: A promising and cost-effective tool

The cocktail of pesticides sprayed to protect crops generates a miscellaneous and generalized contamination of water bodies. Sorption, especially on soils, regulates the spreading and persistence of these contaminants. Fine resolution sorption data and knowledge of its drivers are needed to manage this contamination. The aim of this study is to investigate the potential of Mid-Infrared spectroscopy (MIR) to predict and specify the adsorption and desorption of a diversity of pesticides. We constituted a set of 37 soils from French mainland and West Indies covering large ranges of texture, organic carbon, minerals and pH. We measured the adsorption and desorption coefficients of glyphosate, 2,4-dichlorophenoxyacetic acid (2,4-D) and difenoconazole and acquired MIR Lab spectra for these soils. We developed Partial Least Square Regression (PLSR) models for the prediction of the sorption coefficients from the MIR spectra. We further identified the most influencing spectral bands and related these to putative organic and mineral functional groups. The prediction performance of the PLSR models was generally high for the adsorption coefficients Kdads (0.4 < R2 < 0.9 & RPIQ >1.8). It was contrasted for the desorption coefficients and related to the magnitude of the desorption hysteresis. The most significant spectral bands in the PLSR differ according to the pesticides indicating contrasted interactions with mineral and organic functional groups. Glyphosate interacts primarily with polar mineral groups (OH) and difenoconazole with hydrophobic organic groups (CH2, CC, COO−, C–O, C–O–C). 2,4-D has both positive and negative interactions with these groups. Finally, this work suggests that MIR combined with PLSR is a promising and cost-effective tool. It allows both the prediction of adsorption and desorption parameters and the specification of these mechanisms for a diversity of pesticides including polar active ingredients.

SVR Chemometrics to Quantify β-Lactoglobulin and α-Lactalbumin in Milk Using MIR.

Protein content variation in milk can impact the quality and consistency of dairy products, necessitating access to in-line real time monitoring. Here, we present a chemometric approach for the qualitative and quantitative monitoring of β-lactoglobulin and α-lactalbumin, using mid-infrared spectroscopy (MIR). In this study, we employed Hotelling T2 and Q-residual for outlier detection, automated preprocessing using nippy, conducted wavenumber selection with genetic algorithms, and evaluated four chemometric models, including partial least squares, support vector regression (SVR), ridge, and logistic regression to accurately predict the concentrations of β-lactoglobulin and α-lactalbumin in milk. For the quantitative analysis of these two whey proteins, SVR performed the best to interpret protein concentration from 197 MIR spectra originating from 42 Cornell University samples of preserved pasteurized modified milk. The R2 values obtained for β-lactoglobulin and α-lactalbumin using leave one out cross-validation (LOOCV) are 92.8% and 92.7%, respectively, which is the highest correlation reported to date. Our approach introduced a combination of preprocessing automation, genetic algorithm-based wavenumber selection, and used Optuna to optimize the framework for tuning hyperparameters of the chemometric models, resulting in the best chemometric analysis of MIR data to quantitate β-lactoglobulin and α-lactalbumin to date.

Robust plastic waste classification using wavelet transform multi-resolution analysis and convolutional neural networks

Mid-infrared spectroscopy (MIR) using photon up-conversion provides advantages over near-infrared spectroscopy (NIR) for plastic waste recycling, including comparable data collection speed and the ability to detect black plastics. However, high-speed MIR spectra suffer from the presence of significant noise. While convolutional neural networks (CNNs) have been utilized for accurate classification of noisy MIR spectra, the analysis of extracted features by the CNN has received less attention. In this study, we analyzed features extracted by a CNN from high-speed MIR spectra collected at 200 spectra per second. Visualizing salient features through the Grad-CAM method revealed that, although the CNN model achieved 100% accuracy, the predictions were not reliable or robust, as the model is susceptible to noise interference. To address this limitation, we propose a wavelet transform-based multi-resolution analysis (MRA) as a preprocessing method for noisy MIR spectra. We show that MRA reconstruction effectively captures features related to characteristic IR peaks, enabling the CNN model to extract informative features from noisy MIR spectra and significantly improves the prediction fidelity and robustness.

Thermal, structural and spectroscopic properties of powder sheep milk obtained by different drying methods

The thermal, microstructural and spectroscopic properties of sheep's milk powder submitted to atomisation and freeze-drying were evaluated. Composition analysis, mid-infrared spectroscopy (MIR), scanning electron microscopy (SEM) and thermal analysis were performed. The MIR spectra showed similar vibration modes associated with each peak, but with a difference in absorption intensity. The SEM images revealed that the milk powder obtained by the atomisation method produced particles with a spherical and wrinkled shape, while the particles obtained by the freeze-drying method showed a continuous structure. Thermal analysis indicated one endothermic peak and four exothermic peaks for atomised milks, while for freeze-dried milks one endothermic peak and three exothermic peaks. MIR analysis showed that the milk powder obtained through atomisation had higher contents than the milk obtained through freeze-drying. This confirms that the atomisation method is more suitable for obtaining milk powder, mainly because it is a cost-effective technique compared with freeze-drying.

In-line monitoring of protein concentration with MIR spectroscopy during UFDF.

Rapid increase of product titers in upstream processes has presented challenges for downstream processing, where purification costs increase linearly with the increase of the product yield. Hence, innovative solutions are becoming increasingly popular. Process Analytical Technology (PAT) tools, such as spectroscopic techniques, are on the rise due to their capacity to provide real-time, precise analytics. This ensures consistent product quality and increased process understanding, as well as process control. Mid-infrared spectroscopy (MIR) has emerged as a highly promising technique within recent years, owing to its ability to monitor several critical process parameters at the same time and unchallenging spectral analysis and data interpretation. For in-line monitoring, Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) is a method of choice, as it enables reliable measurements in a liquid environment, even though water absorption bands are present in the region of interest. Here, we present MIR spectroscopy as a monitoring tool of critical process parameters in ultrafiltration/diafiltration (UFDF). MIR spectrometer was integrated in the UFDF process in an in-line fashion through a single-use flow cell containing a single bounce silicon ATR crystal. The results indicate that the one-point calibration algorithm applied to the MIR spectra, predicts highly accurate protein concentrations, as compared with validated offline analytical methods.

Phenotypic and genetic characterization of the occurrence of noncoagulating milk in dairy sheep

Milk coagulation ability is of central importance for the sheep dairy industry because almost all sheep milk is destined for cheese processing. The occurrence of milk with impaired coagulation properties is an obstacle to cheese processing and, in turn, to the profitability of the dairy companies. In this work, we investigated the causes of noncoagulation of sheep milk; specifically, we studied the effect of milk physicochemical properties on milk coagulation status [coagulating and noncoagulating (NC) milk samples, which do or do not coagulate within 30 min, respectively], and whether mid-infrared spectroscopy (MIR) could be used to assess variability in coagulation status. We also investigated the genetic background of milk coagulation ability. Individual milk samples were collected from 996 Sarda ewes farmed in 47 flocks located in Sardinia (Italy). Considered traits were daily milk yield, milk composition traits, and milk coagulation properties (rennet coagulation time, curd firming time, and curd firmness), and MIR spectra were acquired. About 9% of samples did not coagulate within 30 min. A logistic regression approach was used to test the effect of milk-related traits on milk coagulation status. A principal component (PC) analysis was carried out on the milk MIR spectra, and PC scores were then used as covariates in a logistic regression model to assess their relationship with milk coagulation status. Results of the present work demonstrated that the probability of having NC samples increases as milk contents of proteins and chlorides and somatic cell score increase. The analysis of PC extracted from milk spectra that influenced coagulation status highlighted key regions associated with lactose and protein concentrations, and others not associated with routinely collected milk composition traits. These results suggest that the occurrence of NC is mostly related to damage of the epithelium secretory mammary cells, which occurs with the advancement of a lactation or due to unhealthy mammary gland status. Genetic analysis of milk coagulation status and of the extracted PC confirmed the genetic background of the milk coagulability of sheep milk.

Predicting silicon, aluminum, and iron oxides contents in soil using computer vision and infrared

Silicon, aluminum, and iron oxides are very abundant in soil. Their quantification is important for soil classification, which is a relevant information for the sustainable use and management of soils. In soil laboratories the determination of these oxides, using standard methods, is destructive, costly, laborious, and time consuming. This article presents two analytical methods to quantify SiO2, Al2O3, and Fe2O3 in soil samples using computer vision (COMPVIS) and mid-infrared spectroscopy (MIR). These two methods were developed using 52 soil samples from four states of Brazil. Digital images and MIR spectra were correlated with oxides contents quantified by atomic absorption spectroscopy (AAS) after acid digestion using three multivariate calibration methods: PLS, SPA-MLR, and LS-SVM. This the first time that soil image data has been correlated to silicon and aluminum oxides and the proposed method found excellent correlation values (r2 ranging from 0.95 to 0.99). With the exception of SiO2, MIR resulted in similar predictions to the COMPVIS method’s. LS-SVM presented r2 higher than 0.95 for all oxides estimates. The developed analyses are low cost, fast, and environmentally sustainables.

Genetic parameters of blood urea nitrogen and milk urea nitrogen concentration in dairy cattle managed in pasture-based production systems of New Zealand and Australia

Context Urinary nitrogen excretion by grazing cattle causes environmental pollution. Selecting for cows with a lower concentration of urinary nitrogen excretion may reduce the environmental impact. While urinary nitrogen excretion is difficult to measure, blood urea nitrogen (BUN), mid-infrared spectroscopy (MIR)-predicted BUN (MBUN), which is predicted from MIR spectra measured on milk samples, and milk urea nitrogen (MUN) are potential indicator traits. Australia and New Zealand have increasing datasets of cows with urea records, with 18 120 and 15 754 cows with urea records in Australia and New Zealand respectively. A collaboration between Australia and New Zealand could further increase the size of the dataset by sharing data. Aims Our aims were to estimate genetic parameters for urea traits within country, and genetic correlations between countries to gauge the benefit of having a joint reference population for genomic prediction of an indicator trait that is potentially suitable for selection to reduce urinary nitrogen excretion for both countries. Methods Genetic parameters were estimated within country (Australia and New Zealand) in Holstein, Jersey and a multibreed population, for BUN, MBUN and MUN in Australia and MUN in New Zealand, using high-density genotypes. Genetic correlations were also estimated between the urea traits recorded in Australia and MUN in New Zealand. Analyses used the first record available for each cow or within days-in-milk (DIM) intervals. Key results Heritabilities ranged from 0.08 to 0.32 for the various urea traits. Higher heritabilities were obtained for Jersey than for Holstein, and for the New Zealand cows than for the Australian cows. While urea traits were highly correlated within Australia (0.71–0.94), genetic correlations between Australia and New Zealand were small to moderate (0.08–0.58). Conclusions Our results showed that the heritability for urea traits differs among trait, breed, and country. While urea traits are highly correlated within country, genetic correlations between urea traits in Australia and MUN in New Zealand were only low to moderate. Implications Further study is required to identify the underlying causes of the difference in heritabilities observed, to compare the accuracies of different reference populations, and to estimate genetic correlations between urea traits and other traits such as fertility and feed intake. Larger datasets may help estimate genetic correlations more accurately between countries.

Water Interactions in Hybrid Polyacrylate-Silicate Hydrogel Systems.

Hybrid polyacrylate-silicate hydrogels were obtained in the presence of N,N′-methylenebisacrylamide (NNMBA) as the cross-linking monomer and sodium thiosulphate/potassium persulphate (NTS/KPS) as the redox initiators. The results of the tests allowed us to conclude that a hybrid structure with a polyacrylate scaffolding and a silicate matrix had been obtained. The results of the rheological analysis revealed that the hydrogel sample with a 1:7 mass ratio of sodium water glass to the sodium polyacrylate is characterized by the highest complex viscosity. Thermal analysis (Thermogravimetry/Differential Scanning Calorimetry (TG/DSC)) showed that water begins to evaporate at higher temperatures, from 120 °C to even 180 °C. These results were confirmed by mid-infrared spectroscopy (MIR) and nuclear magnetic resonance spectroscopy (NMR) analysis. Differences in the intensity of the peaks derived from water in the MIR spectra indicate that most of the water is bounded. In turn, NMR results showed that the mobility of water molecules decreases as the amount of sodium water glass in the mixture increases.

Soil respiration and its temperature sensitivity (Q10): Rapid acquisition using mid-infrared spectroscopy

Spatial patterns of soil respiration (SR) and its sensitivity to temperature (Q10) are one of the key uncertainties in climate change research but since their assessment is very time-consuming, large data sets can still not be provided. Here, we investigated the potential of mid-infrared spectroscopy (MIRS) to predict SR and Q10 values for 124 soil samples of diverse land use types taken from a 2868 km2 catchment (Rur catchment, Germany/Belgium/Netherlands). Soil respiration at standardized temperature (25 °C) and soil moisture (45% of maximum water holding capacity, WHC) was successfully predicted by MIRS coupled with partial least square regression (PLSR, R2 = 0.83). Also the Q10 value was predictable by MIRS-PLSR for a grassland submodel (R2 = 0.75) and a cropland submodel (R2 = 0.72) but not for forested sites (R2 = 0.03). In order to provide soil respiration estimates for arbitrary conditions of temperature and soil moisture, more flexible models are required that can handle nonlinear and interacting relations. Therefore, we applied a Random Forest model, which includes the MIRS spectra, temperature, soil moisture, and land use as predictor variables. We could show that SR can be simultaneously predicted for any temperature (5–25 °C) and soil moisture level (30–75% of WHC), indicated by a high R2 of 0.73. We conclude that the combination of MIRS with sophisticated statistical prediction tools allows for a novel, rapid acquisition of SR and Q10 values across landscapes and thus to fill an important data gap in the validation of large scale carbon modeling.

Chemical Indicators of Cryoturbation and Microbial Processing throughout an Alaskan Permafrost Soil Depth Profile

Although permafrost soils contain vast stores of organic C, relatively little is known about the chemical composition of their constituent soil organic matter (SOM). Mineral permafrost and organic (OAL) and mineral active layer (MAL) soils from Sagwon Hills, AK were analyzed for total C and N content and SOM chemical composition using Fourier transformed mid‐infrared spectroscopy (MidIR). We also investigated techniques for proper collection of MidIR spectra on high C soils, such as permafrost. Principal Components Analysis (PCA) of the MidIR spectra revealed that the OAL was different from the MAL and permafrost based on absorbance of various organic functional groups, such as hydroxyls, alkyls, carbonyls, amines, amides, and esters. The top of the permafrost (0–15 cm below the maximum active layer thaw depth) was also differentiated from the deeper permafrost (16–40 cm below) by the same organic functional groups. Spectral data suggested that there is more chemically labile C (e.g., hydroxyl, amine groups, carbohydrates) in the OAL than the top of the permafrost, which in turn has more labile C than the MAL and deeper permafrost. The chemical similarity between the top of the permafrost and the OAL, and its differences with the MAL, suggest that organic matter (OM) is introduced into the permafrost through cryoturbation. All the soils showed evidence of microbial processing, such as organic acids and carboxylates, however the relative abundance of these compounds varied by soil depth. This study advances our understanding of permafrost C chemistry and the reactivity of constituent compounds.

Technical note: Improving the accuracy of mid-infrared prediction models by selecting the most informative wavelengths

Mid-infrared spectroscopy (MIRS) is widely used to collect milk phenotypes at the population level. The aim of this study was to test the ability of the uninformative variable elimination (UVE) method to select and remove uninformative wavelength variables before partial least squares (PLS) analysis. Milk titratable acidity (TA) and Ca content were used as examples to illustrate the procedure. Reference values and MIRS spectra (n=208) of TA and Ca were retrieved from an existing database. The data set was randomly divided into calibration (70% of data) and validation (30% of data) sets, and PLS analysis was carried out before and after the UVE procedure. The UVE procedure selected 244 and 113 informative wavelengths for TA and Ca, respectively, from a total of 1,060. The elimination of uninformative variables before PLS regression increased the accuracy of MIRS prediction models, and it substantially reduced the computation time. Dealing with fewer variables is expected to enhance the efficiency of MIRS models to predict phenotypes at population level.

Protein content determination of shiitake mushroom (Lentinus edodes) using mid-infrared spectroscopy and chemometeics

The feasibility of protein determination of shiitake mushroom (Lentinus edodes) using mid-infrared spectroscopy (MIR) was studied in the present paper. Wavenumbers 3 581-689 cm(-1) were used for quantitative analysis of protein content after removing of the part of obvious noises. Five points Savitzky-Golay smoothing was applied to pretreat the MIR spectra and partial least squares (PLS) model was built based on the pretreated spectra. The full spectra PLS model obtained poor performance with the ratio of prediction to deviation (RPD) of only 1.77. Successive projections algorithm (SPA) was applied to select 7 sensitive wavenumbers from the full spectra, and PLS model, multiple linear regression (MLR), back-propagation neural network (BPNN) and extreme learning machine (ELM) model were built using the selected sensitive wavenumbers. SPA-PLS model and SPA-MLR model obtained relatively worse results than SPA-BPNN model and SPA-ELM model. SPA-ELM obtained the best results with correlation coefficient of prediction (R(p)) of 0.899 5, root mean square error of prediction (RMSEP) of 1.431 3 and RPD of 2.18. The overall results indicated that MIR combined with chemometrics could be used for protein content determination of shiitake mushroom, and SPA could select sensitive wavenumbers to build more accurate models instead of the full spectra.

Mid-infrared attenuated total reflectance spectroscopy for soil carbon and particle size determination

In soil analysis mid-infrared spectroscopy (MIR) is not as widely used as visible and near infrared spectroscopy (VisNIR), mainly due to (1) the need for sample preparation and (2) strong absorption leading to spectral distortion and total absorption. In this study we used attenuated total reflectance (ATR) as a sample presentation technique to obtain MIR spectra of neat soil samples to overcome the two aforementioned drawbacks. A set of diverse soil samples (N=270) were scanned with a Fourier Transform IR spectrometer (4000 to 400cm−1) on its diamond ATR crystal. The objective was to investigate the usefulness of ATR spectra in determining clay, sand, organic and inorganic C using partial least squares regression. Results showed that inorganic C and clay can be predicted very well with ATR, with validation R2 around 0.94 and Ratio of Performance to Deviation (RPD) around 4.0. Sand can be predicted satisfactorily also, with R2=0.88 and RPD slightly lower than 3.0. Organic C is predicted with R2=0.77 and RPD>2.1. Compared to the results of VisNIR models, significantly higher accuracy was obtained for clay, sand, and inorganic C, and a slight improvement was obtained for organic C. In our opinion, MIR-ATR can be a promising and powerful tool for soil characterization. It combines the advantages of both VisNIR (minimum sample preparation and high analysis throughput) and diffuse reflectance MIR (better model performance). Finally, as evidenced in our “leave-whole-field-out” experiment, the models developed with ATR spectra performed better on dissimilar samples and might have a larger scope of application than VisNIR.

Diffuse-Reflectance Fourier-Transform Mid-Infrared Spectroscopy as a Method of Characterizing Changes in Soil Organic Matter

Diffuse-reflectance Fourier-transform mid-infrared spectroscopy (MidIR) can identify the presence of important organic functional groups in soil organic matter (SOM); however, spectral interpretation needs to be validated to correctly assess changes in SOM quality and quantity. We amended soils with known standards, increasing the total C in the sample by 50%, and measured changes in MidIR spectra. Adenine, casein, cellulose, ergosterol, glucosamine, glycine, guanine, indole, methionine, palmitic acid, egg protein, chlorophyllin, tannic acid, xylose, urease, and vanillin standards were used. In addition, corn (Zea mays L.) stalk feedstock and two chars produced at different temperatures were studied. Two soils were used: a Hoytville, OH, soil (2.5% C and 36% clay) and an Akron, CO, soil (1.5% C and 14% clay). The addition of standards with >10% N content resulted in increased amide-like absorbance at 1670, 1588, and 1513 cm−1. Bands at 2970 to 2800, 2200 to 2000, and 1030 to 1160 cm−1 were sensitive to added polysaccharide. Protein addition increased absorption at 2970 to 2800 cm−1 but also increased the 1691 and 1547 cm−1 amide bands. Vanillin addition resulted in higher absorbance at the 1592, 1515, and 1295 cm−1 aromatic C=C bands. Biochars produced at 300°C resulted in increased absorbance at carbonyl and aliphatic bands, while addition of 500°C biochar increased aromatic absorbance. Our results showed that MidIR is sensitive to relatively small changes in SOM. If assumptions about the soil mineralogy are met, specific spectral bands can be used to follow changes in SOM chemistry.

Use of mid- and near-infrared spectroscopy to track degradation of bio-based eating utensils during composting

Near-infrared spectroscopy (NIRS) and mid-infrared spectroscopy (MIRS) have been used for quantitative and/or qualitative analysis of a wide range of materials. The objective of this study was to investigate the potential of MIRS and NIRS for following the degradation of bio-based food utensils during composting. Polylactide (PLA)–based forks lost 34% of their initial mass and were reduced to small friable fragments after 7weeks of composting. NIRS and MIRS spectra of forks that were incubated for 7weeks were nearly identical to spectra of untreated forks. NIRS and MIRS were more useful in following the degradation of a starch/polypropylene (PP) polymer. Spectral results demonstrated that the starch component degraded during composting and that the PP component was recalcitrant. These results confirm that MIRS and NIRS are useful in determining the composition of biobased materials. However, the spectra did not provide useful information about the extent of PLA polymer degradation.

Mid infrared and fluorescence spectroscopies coupled with factorial discriminant analysis technique to identify sheep milk from different feeding systems

Mid infrared spectroscopy (MIR) combined with multivariate data analysis was used to discriminate between ewes milk samples according to their feeding systems (controls, ewes fed scotch bean and ewes fed soybean). The MIR spectra were scanned throughout the first 11weeks of the lactation stage. When factorial discriminant analysis (FDA) with leave one-out cross-validation was applied, separately, to the three spectral regions in the MIR (i.e. 3000–2800, 1700–1500 and 1500–900cm−1), the classification rate was not satisfactory. Therefore, the first principal component (PCs) scores (corresponding to 3, 10 and 10 for, respectively, the 3000–2800, 1700–1500 and 1500–900cm−1) of the principal component analysis (PCA) extracted from each of the data sets were pooled (concatenated) into a single matrix and analysed by FDA. Correct classification amounting to 71.7% was obtained. Finally, the same procedure was applied to the MIR and fluorescence data sets and 98% of milk samples were found to be correctly classified. Milk samples belonging to control and soybean groups were 100% correctly classified. Regarding milk samples originating from the scotch bean group, only 2 out of 33 samples were misclassified. It was concluded that concatenation of the data sets collected from the two spectroscopic techniques is an efficient tool for authenticating milk samples according to their feeding systems, regardless of the lactation stage.

Mid-infrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk

The aims of this study were to investigate variation of milk coagulation property (MCP) measures and their predictions obtained by mid-infrared spectroscopy (MIR), to investigate the genetic relationship between measures of MCP and MIR predictions, and to estimate the expected response from a breeding program focusing on the enhancement of MCP using MIR predictions as indicator traits. Individual milk samples were collected from 1,200 Brown Swiss cows (progeny of 50 artificial insemination sires) reared in 30 herds located in northern Italy. Rennet coagulation time (RCT, min) and curd firmness (a30, mm) were measured using a computerized renneting meter. The MIR data were recorded over the spectral range of 4,000 to 900cm−1. Prediction models for RCT and a30 based on MIR spectra were developed using partial least squares regression. A cross-validation procedure was carried out. The procedure involved the partition of available data into 2 subsets: a calibration subset and a test subset. The calibration subset was used to develop a calibration equation able to predict individual MCP phenotypes using MIR spectra. The test subset was used to validate the calibration equation and to estimate heritabilities and genetic correlations for measured MCP and their predictions obtained from MIR spectra and the calibration equation. Point estimates of heritability ranged from 0.30 to 0.34 and from 0.22 to 0.24 for RCT and a30, respectively. Heritability estimates for MCP predictions were larger than those obtained for measured MCP. Estimated genetic correlations between measures and predictions of RCT were very high and ranged from 0.91 to 0.96. Estimates of the genetic correlation between measures and predictions of a30 were large and ranged from 0.71 to 0.87. Predictions of MCP provided by MIR techniques can be proposed as indicator traits for the genetic enhancement of MCP. The expected response of RCT and a30 ensured by the selection using MIR predictions as indicator traits was equal to or slightly less than the response achievable through a single measurement of these traits. Breeding strategies for the enhancement of MCP based on MIR predictions as indicator traits could be easily and immediately implemented for dairy cattle populations where routine acquisition of spectra from individual milk samples is already performed.

Reproducibility and Repeatability of Measures of Milk Coagulation Properties and Predictive Ability of Mid-Infrared Reflectance Spectroscopy

The objectives of the study were to estimate the reproducibility and repeatability of milk coagulation properties (MCP) measured by a computerized renneting meter (CRM) and to evaluate the predictive ability of mid-infrared spectroscopy (MIRS) as an innovative technology for the assessment of rennet coagulation time (RCT, min) and curd firmness (a30, mm). Four samples without addition of preservative (NP) and 4 samples with Bronopol addition (PS) were collected from each of 83 Holstein-Friesian cows. Six hours after collection, 2 replicated measures of MCP were obtained with CRM using 1 NP and 1 PS sample from each cow. Mid-infrared spectra of the remaining NP and PS samples from each animal were recorded after 6h, 4 d, and 8 d after sampling. Two groups of calibration equations were developed using MIRS spectra and CRM measures of MCP as reference data obtained from analysis of NP and PS, respectively. Reproducibility and repeatability of CRM measures were obtained from REML estimation of variance components on the basis of a linear model including the fixed effects of herd and days in milk class and the random effects of cows, sample treatment (addition or no addition of preservative), and the interaction between cow and sample treatment. Coefficient of reproducibility is an indicator of the agreement between 2 measurements of MCP for the same milk sample preserved with or without addition of Bronopol. Coefficient of repeatability is an indicator of the agreement between repeated measures of MCP. Pearson correlations between MCP measures for NP and PS were 0.97 and 0.83 for RCT and a30, respectively. Reproducibility of CRM measures under different preserving conditions of milk was 93.5% for RCT and 64.6% for a30. Repeatabilities of RCT and a30 measures were 95.7 and 77.3%, respectively. Based on the estimated cross-validation standard errors and coefficients of determination and ratios of standard errors of cross-validation to standard deviation of reference data, the predictive ability of MIRS calibration equations was moderate for RCT and unsatisfactory for a30. Predictive ability of equations based on spectra and MCP measures of PS was greater than that of equations based on data of NP. The study did not provide conclusive evidence on the effectiveness of MIRS as a predictive tool for MCP and it requires an enlargement of the variability of milk sampling circumstances. Because the relevance of MIRS predictions in relation to breeding programs for MCP based on indicator traits relies on the genetic variation of MIRS predictions and on phenotypic and genetic correlations between MIRS predictions and MCP measures, additional specific investigations on these topics are needed.

Structural and conformational changes during thermally‐induced crystallization of poly(trimethylene terephthalate) by infrared spectroscopy

AbstractConformational changes occurring during thermally‐induced crystallization of poly(trimethylene terephthalate) (PTT) by annealing have been studied using density measurement, differential scanning calorimetry (DSC), and mid‐infrared spectroscopy (MIR). Infrared spectra of amorphous and semicrystalline PTT were obtained, and digital subtraction of the amorphous contribution from the semicrystalline PTT spectra provided characteristic MIR spectra of amorphous and crystalline PTT. The normalized absorbance of 1577, 1173, and 976 cm−1 were plotted against the crystallinity showing that these bands can be used unambiguously to represent the trans conformation while the band at 1358 cm−1 can be used to represent gauche conformation of methylene segment. The presence of a weak band at 1358 cm−1 in the amorphous spectrum suggested that a small amount of gauche conformation is present in the amorphous phase. Infrared spectroscopy has been used for the first time as a means to estimate the trans and gauche conformations of methylene segments in PTT as a function of Ta. The amount of gauche conformation was plotted against the crystalline fraction and the extrapolation of this plot to zero crystalline fraction provided a value of 0.07, suggested that the pure amorphous phase consist of ∼ 7% gauche conformation. It was found that the amorphous and crystalline gauche conformation increases at the expense of amorphous trans conformation during thermally induced crystallization of PTT. © 2008 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 46: 1497–1504, 2008