Development and validation of a near-infrared spectroscopy model for the prediction of muscle protein in Chinese native chickens.

This study investigated the ability of the near-infrared spectroscopy (NIRS) model to predict the protein of freeze-dried muscle samples in Chinese native chickens and to determine the accuracy of the models for other native chicken breeds. Spectral pretreatment, wavelength selection, and outlier sample elimination were used to optimize the calibration models. The results showed that the best model was obtained by using a combination of standard normal variable transformation and gap-segment first-derivative pretreatment spectra after removing 48 outliers in the wavelength range of 1,439 to 1,900 nm, with coefficient of determination for the calibration (R2C) of 0.95, standard error of cross-validation (SECV) of 1.18, coefficient of determination for the prediction (R2P) of 0.95, the ratio of the standard deviation of the validation to the standard deviation of the calibration (RPDP) of 4.62. The findings indicated that NIRS can be used to predict the protein of freeze-dried muscle in Chinese native chickens.

Use of real-time near-infrared fluorescence to assess gastric viability in dogs with gastric dilatation volvulus: A case-control study.

OBJECTIVE: To describe near-infrared fluorescence (NIRF) for assessment of gastric viability and describe NIRF's influence on the surgeon's operative strategy in dogs with gastric dilatation and volvulus (GDV). STUDY DESIGN: Prospective clinical trial. ANIMALS: Twenty dogs with GDV and 20 systemically healthy dogs. METHODS: Following gastric derotation, the surgeon's subjective assessment of gastric viability was recorded prior to near-infrared imaging. Changes in the surgeon's initial assessment of viability based on the visual pattern of gastric fluorescence was recorded. If nonviable (lack of defined vessels), a partial gastrectomy was performed and submitted for histopathology. The stapled gastrectomy line was imaged. Viable (defined vessels) and nonviable fluorescence intensities were compared with healthy dogs undergoing surgery for nongastrointestinal disease. RESULTS: Subjective assessment diagnosed 17 viable and three nonviable GDVs (2 fundi; 1 cardia). Near-infrared imaging demonstrated nonviable gastric fluorescence in 4 dogs (3 fundi/cardia; 1 fundus). The surgeon's margins for resection were altered in 3/20 dogs. Fluorescence intensity (cardia, fundus, body, pylorus) was lower in GDV viable (30.59%, p = .04; 38.17%, p < .01; 51.18%, p < .01; 44.12%, p= .01) and nonviable (11.00%, p < .01; 4.33%, p < .01; 57.67%, p = .22; 54.33%, p = .72) dogs compared to healthy controls (44.7%, 70.05%, 84.00%, 63.95%). Fundic fluorescence was less in nonviable gastric tissue in comparison with viable gastric tissue (p = .03). Fluorescence of the gastrectomy staple line approximated that of viable tissue. CONCLUSION: Near-infrared fluorescence can identify histologically confirmed nonviable gastric tissue. CLINICAL SIGNIFICANCE: These results provide enough evidence to support the implementation of NIRF as an adjunct to gross examination of the gastric wall in dogs with GDV.

The impact of marker selection, in-line near-infrared spectroscopy (NIR), and feed mix time on the coefficient of variation (mix uniformity), body weight uniformity and broiler growth performance during the starter, grower, and finisher periods.

Most feed manufacturers in the United States use the same mixing time (and thus mix uniformity) throughout the growing period regardless of age and consumption patterns. However, research evaluating the optimum mixing time requirements and novel analysis methods, such as in-line near-infrared (NIR) spectroscopy, on the coefficient of variation (CV) and growth performance of broilers throughout the production phases is sparse. Two experiments were conducted to determine the effects of marker selection, in-line NIR, and varying mix times on mix uniformity, broiler growth performance, and body weight uniformity from 1 to 42 d of age. Feed was manufactured utilizing a 1,815-kg counterpoise ribbon mixer. In both experiments, feed was mixed for 4.5 min (3 min dry mix and 90 s of wet mix) and 30 s (0 s dry mix and 30 s wet mix) to obtain a standard mix (SM) and an abbreviated mix (AM), respectively. Experiment 1 constituted a 2 × 2 × 4 factorial arrangement of 2 mix times, (4.5 and 0.5 min), 2 batch sizes (908 and 1,815 kg), and 4 methodologies to evaluate mixer performance (sodium chloride, Microtracers (Red#40 and Blue#40), and in-line NIR). In experiment 2, broilers received different mix time combinations: 1) SM from 1 to 42 d, 2) SM from 1 to 28 d and AM from 28 to 42 d, 3) SM from 1 to 14 d and AM from 14 to 42 d, and 4) AM from 1 to 42 d. In both experiments, selecting a single source marker provided a more accurate estimation of mixer CV in SM and AM diets (P < 0.05). In experiment 2, mix time did not influence BW, feed intake (FI), FCR, or individual bird BW CV from 1 to 42 d of age (P > 0.05). These data indicated that mixer CV differed depending on total mix time and methodology used and diets with a reduced mix time may not necessarily influence growth performance and BW uniformity during the starter, grower, and finisher periods of broilers.

Minerals and essential amino acids of bovine colostrum: Phenotypic variability and predictive ability of mid- and near-infrared spectroscopy.

Colostrum quality and volume are fundamental for calves because it is the primary supplier of antibodies and the first source of energy, carbohydrates, lipids, proteins, minerals, and vitamins for the newborn. Assessing the detailed composition (i.e., AA and mineral content) of bovine colostrum (BC) on-line and at a reasonable cost would help dairy stakeholders such as farmers or veterinarians for precision feeding purposes and industries producing preparations containing BC such as foodstuff, supplements, and medicaments. In the present study we evaluated mid- (MIRS) and near-infrared spectroscopy (NIRS) prediction ability for AA and mineral composition of individual BC. Second, we the investigated the major factors affecting the phenotypic variability of such traits also evaluating the correlations with the Ig concentration. Results demonstrated that MIRS and NIRS were able to provide sufficiently accurate predictions for all the AA. The coefficient of determination in external validation (R2V) fell, in fact, within the range of 0.70 to 0.86, with the exception of Ile, His, and Met. Only some minerals reached a sufficient accuracy (i.e., Ca, P, S, and Mg; R2V ≥ 0.66) using MIRS, and also S (R2V = 0.87) using NIRS. Phenotypically, both parity and calving season affected the variability of these BC composition traits. Heifers' colostrum was the one with the greatest concentration of Ca and P, the 2 most abundant minerals. These minerals were however very low in cows calving in summer compared with the rest of the year. The pattern of AA across parities and calving season was not linear, likely because their variability was scarcely (or not) affected by these effects. Finally, samples characterized by high IgG concentration were those presenting on average greater concentration of AA. Findings suggest that infrared spectroscopy has the potential to be used to predict certain AA and minerals, outlining the possibility of implementing on-site analyses for the evaluation of the broad-sense BC quality.

Genetic analyses of nutrient digestibility measured by fecal near-infrared spectroscopy in pigs.

Improved nutrient digestibility is an important trait in genetic improvement in pigs due to global resource scarcity, increased human population and greenhouse gas emissions from pork production. Further, poor nutrient digestibility represents a direct nutrient loss, which affects the profit of the farmer. The aim of this study was to estimate genetic parameters for apparent total tract digestibility of nitrogen (ATTDn), crude fat (ATTDCfat), dry matter (ATTDdm), and organic matter (ATTDom) and to investigate their genetic relationship to other relevant production traits in pigs. Near-infrared spectroscopy was used for prediction of total nitrogen content and crude fat content in feces. The predicted content was used to estimate apparent total tract digestibility of the different nutrients by using an indicator method, where acid insoluble ash was used as an indigestible marker. Average ATTDdm, ATTDom, ATTDn, and ATTDCfat ranged from 61% to 75.3%. Moderate heritabilities was found for all digestibility traits and ranged from 0.15 to 0.22. The genetic correlations among the digestibility traits were high (>0.8), except for ATTDCfat, which had no significant genetic correlation to the other digestibility traits. Significant genetic correlations were found between ATTDn and feed consumption between 40 and 120 kg live weight (F40120) (-0.54 ± 0.11) and ATTDdm and F40120 (-0.35 ± 0.12) and ATTDom and F40120 (-0.28 ± 0.13). No significant genetic correlations were found between digestibility traits and loin depth at 100 kg, nor backfat thickness at 100 kg (BF), except between BF and ATTDn (-0.31 ± 0.14). These results suggested that selection for improved feed efficiency through reduced feed intake within a weight interval, also has led to improved ATTDdm, ATTDom, and ATTDn. Further, the digestibility traits are heritable, but mainly related to feed intake and general function of the intestines, as opposed to allocation of feed resources to different tissues in the body.

Improved nutrient digestibility is an important trait in genetic improvement of pigs due to global resource scarcity, increased human population and greenhouse gas emissions from pork production. The main aim of this study was to investigate whether nutrient digestibility traits in pigs are heritable, and if they are genetically linked to other production traits. The results showed that digestibility of dry matter, organic matter, nitrogen, and crude fat are heritable, and can be selected for in a pig breeding program. The traits are genetically linked to other relevant production traits, such as feed intake, but not to carcass traits, such as loin depth. The results suggest that nutrient digestibility are traits that can be selected for, and that the traits are under indirect selection through other traits in the pig breeding program. The results also indicate that the nutrient digestibility traits express how well the animal utilizes consumed feed, rather than allocating feed to different tissue deposition.

A review of the recent advances for the in ovo sexing of chicken embryos using optical sensing techniques.

The culling of day-old male chicks has caused ethical and economic concerns. Traditional approaches for detecting the in ovo sex of chicken embryos involve opening the eggshell and inner membrane, which are destructive, time-consuming, and inefficient. Therefore, noncontact optical sensing techniques have been examined for the in ovo sexing of chicken embryos. Compared with traditional methods, optical sensing can increase determination throughput and frequency for the rapid sexing of chicken embryos. This paper presented a comprehensive review of the different optical sensing techniques used for the in ovo sexing of chicken embryos, including visible and near-infrared (Vis-NIR) spectroscopy, hyperspectral imaging, Raman spectroscopy, fluorescence spectroscopy, and machine vision, discussing their advantages and disadvantages. In addition, the latest research regarding different detection algorithms and models for the in ovo sexing of chicken embryos was summarized. Therefore, this paper provides updated information regarding the optical sensing techniques that can be used in the poultry industry and related research.

Use of near-infrared reflectance spectroscopy on feces to estimate digestibility and dry matter intake of dietary nutritional characteristics under grazing conditions in Colombian creole steers.

Digestibility and intake are parameters difficult and expensive to estimate under grazing conditions; therefore, the aim of this study was to develop near-infrared reflectance spectroscopy (NIRS) calibrations applied to feces (F-NIRS) and evaluate their accuracy to predict dry matter digestibility (DMD) and dry matter intake (DMI) of Colombian creole cattle. Five digestibility trials using creole steers were conducted; indigestible neutral detergent fiber (iNDF) was used as internal marker and Cr2O3 and TiO2 as external markers. A total of 249 forage and 396 fecal samples from individual animals were collected, dried, and grinded for conventional chemical analysis. For spectral analysis, fecal samples were pooled across collection periods (77 samples). Chemometric analysis was performed using WinISI V4.10 software applying the modified partial least squares method. Cross-validation was performed to avoid overfitting the models. The goodness-of-fit statistics considered were the coefficient of determination in cross-validation and prediction sets (R2cv and r2, respectively) and the ratio performance deviation (RPD). Fecal NIRS calibrations developed for forage and supplement DMD showed a satisfactory fit (R2cv =0.87 and RPD=2.77 and R2cv=0.92 and RPD=3.50, respectively). The accuracy of fecal output equations using chromium (Cr) and titanium (Ti) was similar in terms of R2cv (0.92) and RPD (3.63 vs. 3.57). Total DMI equations using Ti performed better compared to Cr (R2cv = 0.82 vs. 0.78; RPD=2.41 vs. 2.17, respectively). The F-NIRS models were validated using a completely independent set of fecal samples showing a moderate fit (r2>0.8 and RPD>2.0). This study showed that F-NIRS is a feasible tool to predict DMD and DMI of creole steers under grazing conditions. However, previous to socialization, this requires an improvement in accuracy of the calibrated equations related to grazing animals in different production contexts.

Near-infrared spectroscopy for rapid evaluation of winter cereals and Italian ryegrass forage mixtures.

Near-infrared (NIR) spectroscopy was employed to determine the differences between forage mixtures of winter cereals and Italian ryegrass and to evaluate fermentation characteristics of mixed silages. Forages were harvested on five phases (Cuts 1-5), with 1 week interval (n = 100). The yield of the last harvest (Cut 5) was ensiled and analyzed on four different days (D0, D7, D14, and D90) (n = 80). Principal component analysis based on the NIR data revealed differences according to the days of harvest, differences between winter cereals and Italian ryegrass forages, and differences in the fermentation stages of silages. The partial least square regression models for crude protein (CP), crude fiber (CF), and ash gave excellent determination coefficient in cross-validation (R2 CV > 0.9), while models for ether extract (EE) and total sugar content were weaker (R2 CV = 0.87 and 0.74, respectively). The values of root mean square error of cross-validation were 0.59, 0.76, 0.22, 0.31, and 2.36 %DM, for CP, CF, EE, ash, and total sugar, respectively. NIR proved to be an efficient tool in evaluating type and growth differences of the winter cereals and Italian ryegrass forage mixtures and the quality changes that occur during ensiling.

Non-invasive classification of single and double-yolk eggs using Vis-NIR spectroscopy and multivariate analysis.

1. This study was conducted to develop an efficient technique for separating double-yolked (DY) from single-yolked (SY) light brown broiler eggs with comparable shape and size, that were hard to distinguish merely by their external characteristics, using Vis-NIR transmission spectroscopy combined with multivariate analysis.2. Spectroscopic transmission (200-900 nm) was measured after collecting the eggs, and the yolk number was verified by breaking the eggs after boiling. The absorbance of important spectral wavelengths sensitive to yolk amount were identified using feature selection techniques (Principal Component Analysis and Genetic Algorithm).3. Discriminant analysis (DA) and support vector machine (SVM) classifiers were used to develop classification models for DY and SY eggs using the selected important spectral wavelengths.4. When compared to alternative nonlinear techniques, the developed model applying linear discriminant analysis produced greater accuracies in the first (96%) and second (100%) experiments, implying lower inter-egg variability from spectral data and a linear relationship between classes. However, the position and orientation of yolks in DY eggs may limit the classification accuracy of the eggs.

Comparison of near-infrared and dielectric spectra for quantitative identification of bovine colostrum adulterated with mature milk.

The nonhomogeneity of bovine colostrum leads to strong scattering effects for electromagnetic waves, which affects the application of electromagnetic spectroscopy in detecting colostrum. This work aimed to compare the performance of near-infrared spectroscopy (NIRS) and dielectric spectroscopy (DS) in quantitatively predicting the content of mature milk as an adulterant in colostrum. The near-infrared spectra in the range of 833 to 2,500 nm and the dielectric spectra in the range of 20 to 4,500 MHz of 150 adulterated colostrum samples containing 0 to 50% mature milk were analyzed. The different proportions of mature milk in colostrum significantly changed near-infrared and dielectric spectra. The principal component analysis score plot showed that both NIRS and DS could identify the proportion of mature milk in colostrum, but the 2 methods had different characteristics. Linear partial least squares regression and nonlinear least squares support vector machine (LSSVM) models based on near-infrared and dielectric spectra were established to identify doping proportions. The results showed that DS had better identification performance with a root-mean-square error of prediction of 4.9% and a residual prediction deviation of 3.441 by successive projection algorithm LSSVM, whereas NIRS was relatively weak with a root-mean-square error of prediction of 7.3% and a residual prediction deviation of 2.301 by full-spectra LSSVM. This work provides important insights for the quantitative prediction of nonhomogeneous liquid food by DS.

In-line near-infrared analysis of milk coupled with machine learning methods for the daily prediction of blood metabolic profile in dairy cattle.

Precision livestock farming technologies are used to monitor animal health and welfare parameters continuously and in real time in order to optimize nutrition and productivity and to detect health issues at an early stage. The possibility of predicting blood metabolites from milk samples obtained during routine milking by means of infrared spectroscopy has become increasingly attractive. We developed, for the first time, prediction equations for a set of blood metabolites using diverse machine learning methods and milk near-infrared spectra collected by the AfiLab instrument. Our dataset was obtained from 385 Holstein Friesian dairy cows. Stacking ensemble and multi-layer feedforward artificial neural network outperformed the other machine learning methods tested, with a reduction in the root mean square error of between 3 and 6% in most blood parameters. We obtained moderate correlations (r) between the observed and predicted phenotypes for γ-glutamyl transferase (r = 0.58), alkaline phosphatase (0.54), haptoglobin (0.66), globulins (0.61), total reactive oxygen metabolites (0.60) and thiol groups (0.57). The AfiLab instrument has strong potential but may not yet be ready to predict the metabolic stress of dairy cows in practice. Further research is needed to find out methods that allow an improvement in accuracy of prediction equations.

In ovo sexing of eggs from brown breeds with a gender-specific color using visible-near-infrared spectroscopy: effect of incubation day and measurement configuration.

The culling of day-old male chicks is an animal welfare issue within the laying hen industry that raises substantial ethical concern. Alternative methods are sought to pre-select males during embryonic development. This method is called in ovo sexing and allows more humane male culling. A robust and non-invasive in ovo color sexing technique was developed and validated in this research. To this end, visible-near-infrared (vis-NIR) point spectroscopy was used, which has advantages over state-of-the-art hyperspectral imaging in terms of accuracy and cost. Two independent experiments were each conducted on a batch of 600 Isa Brown eggs. These eggs were individually illuminated on d 8 to 14, and d 18 of incubation by a halogen lamp and the signal was measured in the vis-NIR range from 300 to 1,145 nm. Next, optimal preprocessing strategies were applied and partial least squares discriminant analysis (PLS-DA) models were built and further optimized after performing a forward interval partial least squares (FiPLS) for variable selection. Results demonstrated that d 12 is too early for vis-NIR in ovo sexing, resulting in a prediction accuracy of 86.49%. However, after 13 d of incubation, an accuracy of 97.78% was obtained, increasing to 99.52% on d 14. Furthermore, these accuracies were higher than earlier reported percentages from hyperspectral imaging and successful sexing was expedited from d 14 to d 13. Moreover, prediction improvement up to 99.05% was obtained on d 13 by correcting for the variability in eggshell properties using d 0 eggshell corrections. Applying the method on d 18 resulted in a lower accuracy of 94.62% due to stronger light attenuation by the growing embryos. Finally, a reduced spectral range of 749 to 861 nm was found to be sufficient for correct classification of 98.46% of the eggs. This paves the way for high-throughput and cost-efficient usage of smaller and cheaper spectrophotometers in commercial hatcheries.

Transferring a large data library of fresh total mixed rations from a benchtop to 2 portable near-infrared spectrometers for on-farm real-time decisions.

This study was carried out using a spectral database consisting of 394 samples of fresh total mixed ration (TMR) from dairy farms located at Northern Spain. Cloning sets of different size and structure were evaluated for the transfer of the large TMR spectral database obtained on a Foss NIRSystems monochromator to 2 different portable near-infrared devices: one diode array instrument and another based on linear variable filters. The cloning matrix that produced the best matching between instruments was then used to transfer the TMR spectral library to the 2 portable instruments. Once the database had been transferred, calibration equations were developed to compare the predictive ability of the equations obtained in the benchtop and portable instruments. In comparison with the monochromator predictive ability, the calibration equations developed with the near-infrared portable instruments displayed a high and similar accuracy for most of the studied parameters related to TMR composition, enabling their use for predicting TMR quality at the farm level.

Invited review: A comprehensive review of visible and near-infrared spectroscopy for predicting the chemical composition of cheese.

Substantial research has been carried out on rapid, nondestructive, and inexpensive techniques for predicting cheese composition using spectroscopy in the visible and near-infrared radiation range. Moreover, in recent years, new portable and handheld spectrometers have been used to predict chemical composition from spectra captured directly on the cheese surface in dairies, storage facilities, and food plants, removing the need to collect, transport, and process cheese samples. For this review, we selected 71 papers (mainly dealing with prediction of the chemical composition of cheese) and summarized their results, focusing our attention on the major sources of variation in prediction accuracy related to cheese variability, spectrometer and spectra characteristics, and chemometrics techniques. The average coefficient of determination obtained from the validation samples ranged from 86 to 90% for predicting the moisture, fat, and protein contents of cheese, but was lower for predicting NaCl content and cheese pH (79 and 56%, respectively). There was wide variability with respect to all traits in the results of the various studies (standard deviation: 9-30%). This review draws attention to the need for more robust equations for predicting cheese composition in different situations; the calibration data set should consist of representative cheese samples to avoid bias due to an overly specific field of application and ensure the results are not biased for a particular category of cheese. Different spectrometers have different accuracies, which do not seem to depend on the spectrum extension. Furthermore, specific areas of the spectrum-the visible, infrared-A, or infrared-B range-may yield similar results to broad-range spectra; this is because several signals related to cheese composition are distributed along the spectrum. Small, portable instruments have been shown to be viable alternatives to large bench-top instruments. Last, chemometrics (spectra pre-treatment and prediction models) play an important role, especially with regard to difficult-to-predict traits. A proper, fully independent, validation strategy is essential to avoid overoptimistic results.

Prediction of Peking duck intramuscle fat content by near-infrared spectroscopy.

Peking duck is the most representative of the meat-type duck breed, and it is also one of the most popular meats in Asia. Few studies were reported on the fast assessment of duck meat quality. This study aimed to develop a fast measuring of duck fat content by using the near-infrared spectroscopy (NIRS) method. We measured 273 duck breast muscle intramuscle fat (IMF) content and spectra. Partial least-squares regression (PLSR) was used to model the fat content prediction by using the spectra in the wavelengths between 950 and 1650 nm. The best predictive abilities were obtained after the first derivative pretreatment, with coefficient of calibration (R2C) of 0.92, with coefficient of prediction (R2P) of 0.90, ratio performance to deviation (RPD) of 2.72, and ratio of error range (RER) of 15.45, for samples of 30 g duck. Results demonstrated that the near-infrared spectroscopy is a useful tool for fat content assessment of Peking duck meat.

Research Note: Nondestructive detection of super grade chick embryos or hatchlings using near-infrared spectroscopy.

Some unresolved questions in poultry science were addressed: what determines the yield of chick embryos or hatchlings; what kind of influence does egg yolk content have on embryonic development; and how to detect eggs producing super grade chicks? Since the yolk acts as a vital energy and nutrient reservoir for embryos, we hypothesized that a higher yolk content of similar sizes eggs would play an important role in embryo or chick viability during incubation, as well as at hatch. As experimental sample, we used ROSS 308 (broiler line) and a nondestructive spectroscopic absorbance method. The influence of high yolk content to embryonic heartbeat and chick yield (i.e., chick weight/egg weight) were then investigated. Embryonic heartbeat signal was measured indirectly using a prototype near-infrared sensor during incubation period. A positive influence was found in both cases. Similar size eggs with higher yolk content were found to significantly (P-value < 0.05) promote higher chick yield at hatch. This methodology may have the potential to be used to precision poultry production system, ornithology, developmental, or evolutionary biology in the near future.

Ethanol-Soluble Carbohydrates of Cool-Season Grasses: Prediction of Concentration by Near-Infrared Reflectance Spectroscopy (NIRS) and Evaluation of Effects of Cultivar and Management.

Ethanol-soluble carbohydrates (ESCs) of cool-season grasses include mono- and disaccharides and sometimes short-chain fructans, which may exacerbate the risk of pasture-associated laminitis. A calibration for prediction of ESC concentrations by near-infrared reflectance spectroscopy (NIRS) was developed from 323 samples of four cool-season grass species (orchardgrass, Kentucky bluegrass, tall fescue, and perennial ryegrass) across 10 cultivars collected in central Kentucky in the morning and afternoon over two growing seasons. The calibration, which had accuracy above 95%, was used to predict ESC concentrations of 1,532 samples from the second growing season. ESC concentrations increased in the afternoon compared to the morning across all cultivars. In the majority of samples, ESC concentrations were not affected by nitrogen application to plots. Use of NIRS has the potential to evaluate management and cultivar effects on ESC concentrations in cool-season grass pastures.

Prediction of the intramuscular connective tissue components of fresh and freeze-dried samples by near infrared spectroscopy.

This study compared the performance of near-infrared spectroscopy (NIRS) models on fresh and freeze-dried beef muscle samples to predict intramuscular connective tissue (IMCT) components and to determine whether the accuracy of the models differed among different muscles from beef cattle. The hypothesis was that the water content of muscle samples would negatively influence the accuracy of the models, which would differ among muscles. Fresh and freeze-dried samples (n = 171) of four muscles were used to develop NIRS models to predict the contents IMCT. For the total collagen content, the standard error of cross validation (SECV) for model using freeze-dried samples (0.75 mg OH-prol/g DM) was lower than that for model using fresh samples (0.84 mg OH-prol/g DM). For cross-links and proteoglycans, the SECV for models using fresh sample spectra was lower than that for models using freeze-dried sample spectra. The accuracy of the prediction of the models also differed among predicted muscle types.

Partial least squares and machine learning for the prediction of intramuscular fat content of lamb loin.

Given the paucity of lamb carcase grading tools, there is a distinct need for the development of rapid, non-destructive grading tools for Australian lamb carcases, particularly fat content given its importance to meat and eating quality. The aim of the current study was to determine the potential for Near Infrared (NIR) spectroscopy to predict IMF using Partial Least Squares (PLS) and machine learning analysis methods. As such, 299 lamb loins were measured using a NIR fibre optic device, a sample was excised for Soxhlet determination of IMF content and prediction models were created using either PLS or machine learning analyses methods. IMF prediction model outcomes were similar between analysis methods with an R2 = 0.6 and RMSE = 0.84 and R2 = 0.65 and RMSE = 0.72, respectively. This study highlighted that spectra from one slaughter varied greatly from the two succeeding slaughters and wavelengths selected between studies are not consistent.

Intramuscular fat prediction of the semimembranosus muscle in hot lamb carcases using NIR.

The aim of the current study was to identify whether changes in pH or temperature during the development of rigor mortis influence spectral quality and thus the prediction of intramuscular fat (IMF) in hot lamb carcases. Semimembranosus muscles (SM) of 75 lamb carcases were measured pre-rigor 3 times (Runs 1-3) using a near infrared (NIR) spectrometer with muscle pH and temperature also recorded. Calibration models for the prediction of IMF showed that spectra measurements taken at Run 2 (R2 = 0.42, RMSE = 0.75) and Run 3 (R2 = 0.38, RMSE = 0.78) provided more accurate and precise models compared to Run 1 (R2 = 0.27, RMSE = 0.84). An association between spectral variance, internal and surface temperature of the SM was observed across runs. These results suggest that, as muscle temperature declined during the early post-mortem period there was an improved ability of NIR spectra to predict IMF.