Modelling and numerical methods for identifying low-level adulteration in ground beef using near-infrared hyperspectral imaging (NIR-HSI).

Owing to the inherent characteristics of ground beef, adulteration presents a substantial risk for suppliers and consumers alike. This study developed a robust and novel method for identifying replacement fraud in ground beef with beef liver, beef heart, and pork using Near Infrared-Hyperspectral Imaging (NIR-HSI) coupled with chemometric and other statistical methods. More specifically, NIR-HSI provided an efficient and accurate means of identifying each type of adulteration using the classification model Genetic Algorithm (GA) - Backpropagation Artificial Neural Network (BPANN), showing perfect sensitivity and specificity (a value of 1.00) for the calibration and the validation sets for all types of adulteration. As an alternative to chemometric analysis, Hyperspectral Imaging-Root Mean Square (HSI-RMS) value, based on the RMScut-off calculation, was determined to discriminate types of adulterations without the need of resource-intensive modelling. This HSI-RMS approach provides a simple-to-use method that avoids the complexity of HSI data processing and aims to directly understand the similarity between different spectra of one sample in the pixel level. Different types of adulteration show noticeable differences reflected in the HSI-RMS value (varying from 55 to 1439), which demonstrate the potential of HSI-RMS concept as a novel and valuable alternative for assessing the HSI data and facilitating the identification of adulterants.

Interrelationships among physical and chemical traits of cheese: Explanatory latent factors and clustering of 37 categories of cheeses.

Cheese presents extensive variability in physical, chemical, and sensory characteristics according to the variety of processing methods and conditions used to create it. Relationships between the many characteristics of cheeses are known for single cheese types or by comparing a few of them, but not for a large number of cheese types. This case study used the properties recorded on 1,050 different cheeses from 107 producers grouped into 37 categories to analyze and quantify the interrelationships among the chemical and physical properties of many cheese types. The 15 cheese traits considered were ripening length, weight, firmness, adhesiveness, 6 different chemical characteristics, and 5 different color traits. As the 105 correlations between the 15 cheese traits were highly variable, a multivariate analysis was carried out. Four latent explanatory factors were extracted, representing 86% of the covariance matrix: the first factor (38% of covariance) was named Solids because it is mainly linked positively to fat, protein, water-soluble nitrogen, ash, firmness, adhesiveness, and ripening length, and negatively to moisture and lightness; the second factor (24%) was named Hue because it is linked positively to redness/blueness, yellowness/greenness, and chroma, and negatively to hue; the third factor (17%) was named Size because it is linked positively to weight, ripening length, firmness, and protein; and the fourth factor (7%) was named Basicity because it is linked positively to pH. The 37 cheese categories were grouped into 8 clusters and described using the latent factors: the Grana Padano cluster (characterized mainly by high Size scores); hard mountain cheeses (mainly high Solids scores); very soft cheeses (low Solids scores); blue cheeses (high Basicity scores), yellowish cheeses (high Hue scores), and 3 other clusters (soft cheeses, pasta filata and treated rind, and firm mountain cheeses) according to specific combinations of intermediate latent factors and cheese traits. In this case study, the high variability and interdependence of 15 major cheese traits can be substantially explained by only 4 latent factors, allowing us to identify and characterize 8 cheese type clusters.

The use of milk Fourier-transform infrared spectra for predicting cheesemaking traits in Grana Padano Protected Designation of Origin cheese.

The prediction of the cheese yield (%CY) traits for curd, solids, and retained water and the amount of fat, protein, solids, and energy recovered from the milk into the curd (%REC) by Bayesian models, using Fourier-transform infrared spectroscopy (FTIR), can be of significant economic interest to the dairy industry and can contribute to the improvement of the cheese process efficiency. The yields give a quantitative measure of the ratio between weights of the input and output of the process, whereas the nutrient recovery allows to assess the quantitative transfer of a component from milk to cheese (expressed in % of the initial weight). The aims of this study were: (1) to investigate the feasibility of using bulk milk spectra to predict %CY and %REC traits, and (2) to quantify the effect of the dairy industry and the contribution of single-spectrum wavelengths on the prediction accuracy of these traits using vat milk samples destined to the production of Grana Padano Protected Designation of Origin cheese. Information from 72 cheesemaking days (in total, 216 vats) from 3 dairy industries were collected. For each vat, the milk was weighed and analyzed for composition (total solids [TS], lactose, protein, and fat). After 48 h from cheesemaking, each cheese was weighed, and the resulting whey was sampled for composition as well (TS, lactose, protein, and fat). Two spectra from each milk sample were collected in the range between 5,011 and 925 cm-1 and averaged before the data analysis. The calibration models were developed via a Bayesian approach by using the BGLR (Bayesian Generalized Linear Regression) package of R software. The performance of the models was assessed by the coefficient of determination (R2VAL) and the root mean squared error (RMSEVAL) of validation. Random cross-validation (CVL) was applied [80% calibration and 20% validation set] with 10 replicates. Then, a stratified cross-validation (SCV) was performed to assess the effect of the dairy industry on prediction accuracy. The study was repeated using a selection of informative wavelengths to assess the necessity of using whole spectra to optimize prediction accuracy. Results showed the feasibility of using FTIR spectra and Bayesian models to predict cheesemaking traits. The R2VAL values obtained with the CVL procedure were promising in particular for the %CY and %REC for protein, ranging from 0.44 to 0.66 with very low RMSEVAL (from 0.16 to 0.53). Prediction accuracy obtained with the SCV was strongly influenced by the dairy factory industry. The general low values gained with the SCV do not permit a practical application of this approach, but they highlight the importance of building calibration models with a dataset covering the largest possible sample variability. This study also demonstrated that the use of the full FTIR spectra may be redundant for the prediction of the cheesemaking traits and that a specific selection of the most informative wavelengths led to improved prediction accuracy. This could lead to the development of dedicated spectrometers using selected wavelengths with built-in calibrations for the online prediction of these innovative traits.

Cheese yield and nutrients recovery in the curd predicted by Fourier-transform spectra from individual sheep milk samples.

The objectives of this study were to explore the use of Fourier-transform infrared (FTIR) spectroscopy on individual sheep milk samples for predicting cheese-making traits, and to test the effect of the farm variability on their prediction accuracy. For each of 121 ewes from 4 farms, a laboratory model cheese was produced, and 3 actual cheese yield traits (fresh cheese, cheese solids, and cheese water) and 4 milk nutrient recovery traits (fat, protein, total solids, and energy) in the curd were measured. Calibration equations were developed using a Bayesian approach with 2 different scenarios: (1) a random cross-validation (80% calibration; 20% validation set), and (2) a leave-one-out validation (3 farms used as calibration, and the remaining one as validation set) to assess the accuracy of prediction of samples from external farms, not included in calibration set. The best performance was obtained for predicting the yield and recovery of total solids, justifying for the practical application of the method at sheep population and dairy industry levels. Performances for the remaining traits were lower, but still useful for the monitoring of the milk processing in the case of fresh curd and recovery of energy. Insufficient accuracies were found for the recovery of protein and fat, highlighting the complex nature of the relationships among the milk nutrients and their recovery in the curd. The leave-one-out validation procedure, as expected, showed lower prediction accuracies, as a result of the characteristics of the farming systems, which were different between calibration and validation sets. In this regard, the inclusion of information related to the farm could help to improve the prediction accuracy of these traits. Overall, a large contribution to the prediction of the cheese-making traits came from the areas known as "water" and "fingerprint" regions. These findings suggest that, according to the traits studied, the inclusion of water regions for the development of the prediction equation models is fundamental to maintain a high prediction accuracy. However, further studies are necessary to better understand the role of specific absorbance peaks and their contribution to the prediction of cheese-making traits, to offer reliable tools applicable along the dairy ovine chain.

Fixation of pelvic acetabular fractures using 3D-printed fracture plates: a cadaver study.

Open reduction and internal fixation of pelvic acetabular fractures are challenging due to the limited surgical exposure from surrounding abdominal tissue. There have been a number of recent trials using metallic 3D-printed pelvic fracture plates to simplify and improve various elements of these fracture fixation surgeries; however, the amount of time and accuracy involved in the design and implantation of customised plates have not been well characterised. This study recorded the amount of time related to the design, manufacture and implantation of six customised fracture plates for five cadaveric pelvic specimens with acetabular fracture, while manufacturing, and surgical accuracy was calculated from computed tomography imaging. Five of the fracture plates were designed within 9.5 h, while the plate for a pelvis with a pre-existing fracture plate took considerably longer (20.2 h). Manufacturing comprised 3D-printing the plates in Ti6Al4V with a sintered laser melting (SLM) 3D-printer and post-processing (heat treatment, smoothing, tapping threads). The manufacturing times varied from 27.0 to 32.5 h, with longer times related to machining a thread for locking-head screws with a multi-axis computer numerical control (CNC) mill. For the surface of the plate in contact with the bone, the root-mean-square errors of the print varied from 0.10 to 0.49 mm. The upper range of these errors was likely the result of plate designs that were relatively long with thin cross-sections, a combination that gives rise to high thermal stresses when using a SLM 3D-printer. A number of approaches were explored to control the trajectories of locking or non-locking head screws including guides, printed threads or hand-taps; however, the plate with CNC-machined threads was clearly the most accurate with screw angulation errors of 2.77° (range 1.05-6.34°). The implanted position of the plates was determined visually; however, the limited surgical exposure and lack of intra-operative fluoroscopy in the laboratory led to high inaccuracies (translational errors of 1.74-13.00 mm). Plate mal-positioning would lead to increased risk of surgical injury due to misplaced screws; hence, it is recommended that technologies that can control plate positioning such as fluoroscopy or alignment guides need to be implemented into customised plate design and implantation workflow. Due to the plate misalignment and the severe nature of some acetabular fractures comprising numerous small bone fragments, the acetabular reduction exceeded the clinical limit of 2 mm for three pelvises. Although our results indicate that customised plates are unsuitable for acetabular fractures comprising six or more fragments, confirmation of this finding with a greater number of specimens is recommended. The times, accuracy and suggested improvements in the current study may be used to guide future workflows aimed at producing customised pelvic fracture plates for greater numbers of patients.

Goat farm variability affects milk Fourier-transform infrared spectra used for predicting coagulation properties.

Driven by the large amount of goat milk destined for cheese production, and to pioneer the goat cheese industry, the objective of this study was to assess the effect of farm in predicting goat milk-coagulation and curd-firmness traits via Fourier-transform infrared spectroscopy. Spectra from 452 Sarda goats belonging to 14 farms in central and southeast Sardinia (Italy) were collected. A Bayesian linear regression model was used, estimating all spectral wavelengths' effects simultaneously. Three traditional milk-coagulation properties [rennet coagulation time (min), time to curd firmness of 20 mm (min), and curd firmness 30 min after rennet addition (mm)] and 3 curd-firmness measures modeled over time [rennet coagulation time estimated according to curd firmness change over time (RCTeq), instant curd-firming rate constant, and asymptotical curd firmness] were considered. A stratified cross validation (SCV) was assigned, evaluating each farm separately (validation set; VAL) and keeping the remaining farms to train (calibration set) the statistical model. Moreover, a SCV, where 20% of the goats randomly taken (10 replicates per farm) from the VAL farm entered the calibration set, was also considered (SCV80). To assess model performance, coefficient of determination (R2VAL) and the root mean squared error of validation were recorded. The R2VAL varied between 0.14 and 0.45 (instant curd-firming rate constant and RCTeq, respectively), albeit the standard deviation was approximating half of the mean for all the traits. Although average results of the 2 SCV procedures were similar, in SCV80, the maximum R2VAL increased at about 15% across traits, with the highest observed for time to curd firmness of 20 mm (20%) and the lowest for RCTeq (6%). Further investigation evidenced important variability among farms, with R2VAL for some of them being close to 0. Our work outlined the importance of considering the effect of farm when developing Fourier-transform infrared spectroscopy prediction equations for coagulation and curd-firmness traits in goats.

Breed of goat affects the prediction accuracy of milk coagulation properties using Fourier-transform infrared spectroscopy.

The prediction of traditional goat milk coagulation properties (MCP) and curd firmness over time (CFt) parameters via Fourier-transform infrared (FTIR) spectroscopy can be of significant economic interest to the dairy industry and can contribute to the breeding objectives for the genetic improvement of dairy goat breeds. Therefore, the aims of this study were to (1) explore the variability of milk FTIR spectra from 4 goat breeds (Camosciata delle Alpi, Murciano-Granadina, Maltese, and Sarda), and to assess the possible discriminant power of milk FTIR spectra among breeds, (2) assess the viability to predict coagulation traits by using milk FTIR spectra, and (3) quantify the effect of the breed on the prediction accuracy of MCP and CFt parameters. In total, 611 individual goat milk samples were used. Analysis of variance of measured MCP and CFt parameters was carried out using a mixed model including the farm and pendulum as random factors, and breed, parity, and days in milk as fixed factors. Milk spectra for each goat were collected over the spectral range from wavenumber 5,011 to 925 × cm-1. Discriminant analysis of principal components was used to assess the ability of FTIR spectra to identify breed of origin. A Bayesian model was used to calibrate equations for each coagulation trait. The accuracy of the model and the prediction equation was assessed by cross-validation (CRV; 80% training and 20% testing set) and stratified CRV (SCV; 3 breeds in the training set, one breed in the testing set) procedures. Prediction accuracy was assessed by using coefficient of determination of validation (R2VAL), the root mean square error of validation (RMSEVAL), and the ratio performance deviation. Moreover, measured and FTIR predicted traits were compared in the SCV procedure by assessing their least squares means for the breed effect, Pearson correlations, and variance heteroscedasticity. Results showed the feasibility of using FTIR spectra and multivariate analyses to correctly assign milk samples to their breeds of origin. The R2VAL values obtained with the CRV procedure were moderate to high for the majority of coagulation traits, with RMSEVAL and ratio performance deviation values increasing as the coagulation process progresses from rennet addition. Prediction accuracy obtained with the SCV were strongly influenced by the breed, presenting general low values restricting a practical application. In addition, the low Pearson correlation coefficients of Sarda breed for all the traits analyzed, and the heteroscedastic variances of Camosciata delle Alpi, Murciano-Granadina, and Maltese breeds, further indicated that it is fundamental to consider the differences existing among breeds for the prediction of milk coagulation traits.

Prediction of Warner-Bratzler shear force, intramuscular fat, drip-loss and cook-loss in beef via Raman spectroscopy and chemometrics.

Rapid prediction of beef quality remains a challenge for meat processors. This study evaluated the potential of Raman spectroscopy followed by chemometrics for prediction of Warner-Bratzler shear force (WBSF), intramuscular fat (IMF), ultimate pH, drip-loss and cook-loss. PLS regression models were developed based on spectra recorded on frozen-thawed day 2 longissimus thoracis et lumborum muscle and validated using test sets randomly selected 3 times. With the exception of ultimate pH, models presented notable performance in calibration (R2 ranging from 0.5 to 0.9; low RMSEC) and, despite variability in the results, promising predictive ability: WBSF (RMSEP ranging from 4.6 to 9 N), IMF (RMSEP ranging from 0.9 to 1.1%), drip-loss (RMSEP ranging from 1 to 1.3%) and cook-loss (RMSEP ranging from 1.5 to 2.9%). Furthermore, the loading values indicated that the physicochemical variation of the meat influenced the models. Overall, results indicated that Raman spectroscopy is a promising technique for routine quality assessments of IMF and drip-loss, which, with further development and improvement of its accuracy could become a reliable tool for the beef industry.

Prediction of meat quality traits in the abattoir using portable and hand-held near-infrared spectrometers.

The use of near-infrared spectrometers (NIRS) for predicting meat quality traits directly in the abattoir was tested with three trials. For the calibration trial, spectra were acquired from the cross-cut surface of the Longissimus thoracis muscle on 1166 carcasses of Piemontese young bulls with a portable visible-near-infrared spectrometer (Vis-NIRS) and with a small hand-held instrument (Micro-NIRS). A sample of the same muscle was analyzed to provide the reference. Validation statistics of the two instruments were similar. Predictabilities of meat color and purge loss were good, whereas for the other traits they were less promising. The repeatability trial showed that post-slaughter factors, not predictable by NIR spectra collected in the abattoir, affect reference meat quality values. A trial under operative conditions showed that both spectrometers were able to capture the major sources of variation in most of the meat quality traits. Overall, NIRS could be used to predict the animals' "native" characteristics exploitable for genetic improvement of meat quality traits.

Accuracy and biases in predicting the chemical and physical traits of many types of cheeses using different visible and near-infrared spectroscopic techniques and spectrum intervals.

Near-infrared spectroscopy (NIRS) has been widely used to determine various composition traits of many dairy products in the industry. In the last few years, near-infrared (NIR) instruments have become more and more accessible, and now, portable devices can be easily used in the field, allowing the direct measurement of important quality traits. However, the comparison of the predictive performances of different NIR instruments is not simple, and the literature is lacking. These instruments may use different wavelength intervals and calibration procedures, making it difficult to establish whether differences are due to the spectral interval, the chemometric approach, or the instrument's technology. Hence, the aims of this study were (1) to evaluate the prediction accuracy of chemical contents (5 traits), pH, texture (2 traits), and color (5 traits) of 37 categories of cheese; (2) to compare 3 instruments [2 benchtop, working in reflectance (R) and transmittance (T) mode (NIRS-R and NIRS-T, respectively) and 1 portable device (VisNIRS-R)], using their entire spectral ranges (1100-2498, 850-1048, and 350-1830 nm, respectively, for NIRS-R, NIRS-T and VisNIRS-R); (3) to examine different wavelength intervals of the spectrum within instrument, comparing also the common intervals among the 3 instruments; and (4) to determine the presence of bias in predicted traits for specific cheese categories. A Bayesian approach was used to develop 8 calibration models for each of 13 traits. This study confirmed that NIR spectroscopy can be used to predict the chemical composition of a large number of different cheeses, whereas pH and texture traits were poorly predicted. Color showed variable predictability, according to the trait considered, the instrument used, and, within instrument, according to the wavelength intervals. The predictive performance of the VisNIRS-R portable device was generally better than the 2 laboratory NIRS instruments, whether with the entire spectrum or selected intervals. The VisNIRS-R was found suitable for analyzing chemical composition in real time, without the need for sample uptake and processing. Our results also indicated that instrument technology is much more important than the NIR spectral range for accurate prediction equations, but the visible range is useful when predicting color traits, other than lightness. Specifically for certain categories (i.e., caprine, moldy, and fresh cheeses), dedicated calibrations seem to be needed to obtain unbiased and more accurate results.

Technical note: Improving modeling of coagulation, curd firming, and syneresis of sheep milk.

The importance of milk coagulation properties for milk processing, cheese yield, and quality is widely recognized. The use of traditional coagulation traits presents several limitations for testing bovine milk and even more for sheep milk, due to its rapid coagulation and curd firming, and early syneresis of coagulum. The aim of this technical note is to test and improve model fitting for assessing coagulation, curd firming, and syneresis of sheep milk. Using milk samples from 87 Sarda ewes, we performed in duplicate lactodynamographic testing. On each of the 174 analyzed milk aliquots, using 180 observations from each aliquot (one every 15 s for 45 min after rennet addition), we compared 4 different curd firming models as a function of time (CFt, mm) using a nonlinear procedure. The most accurate and informative results were observed using a modified 4-parameter model, structured as follows: [Formula: see text] where t is time, RCTeq (min) is the gelation time, CFP (mm) is the potential asymptotical CF at an infinite time, kCF (%/min) is the curd firming rate constant, and kSR (%/min) is the curd syneresis rate constant. To avoid nonconvergence and computational problems due to interrelations among the equation parameters, CFP was preliminarily defined as a function of maximum observed curd firmness (CFmax, mm) recorded during the analysis. For this model, all the modeling equations of individual sheep milk aliquots were converging, with a negligible standard error of the estimates (coefficient of determination >0.99 for all individual sample equations). Repeatability of the modeled parameters was acceptable, also in the presence of curd syneresis during the lactodynamographic analysis.