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Summary: Germination of Bacillus cereus spores followed by growth and replication of the vegetative cells in food can result in food poisoning and therefore significant economic and health impacts. This study explores a novel approach to detect and differentiate spores and germinated B. cereus cells using hyperspectral imaging (HSI) in combination with machine learning using three different germination triggers. HSI could successfully differentiate between dormant spores, germinated cells and structural controls (non‐spores). The spectral data in the visible‐near‐infrared range are sensitive to unique structural and chemical characteristics specific to spores, setting them apart from their vegetative counterparts and non‐biological controls. This non‐destructive and robust approach shows significant potential for detection and assessment of the physiological state (dormant or germinated). Therefore, HSI is a potential method for the detection of germination in B. cereus spores and merits further research and validation. [ABSTRACT FROM AUTHOR]

Assessment of milk fatty acids has been proposed to support farm management practices including adjustment of feeding regimes and monitoring animal status. This study assessed the performance of a miniaturised near infrared (NIR) sensor for assessment of fatty acids in milk. Samples (n = 483) were from 114 farms, varying in lactation stages within two production seasons and included different dairy breeds. The fatty acids (C4:0, C6:0, C14:0, C16:0, C18:1 c9, SFA, MUFA, SCFA, BCFA) showed Ratio of performance to deviation values higher than two. The performance was equivalent to other independent studies utilising NIR. However, in this study, we used a low‐cost NIR spectrophotometer suitable for on‐the‐farm use. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging (HSI) is a robust and nondestructive method that can detect foreign particles such as microbial, chemical, and physical contamination in food. This review summarizes the work done in the last two decades in this field with a highlight on challenges, risks, and research gaps. Considering the challenges of using HSI on complex matrices like food (e.g., the confounding and masking effects of background signals), application of machine learning and modeling approaches that have been successful in achieving better accuracy as well as increasing the detection limit have also been discussed here. Foodborne microbial contaminants such as bacteria, fungi, viruses, yeast, and protozoa are of interest and concern to food manufacturers due to the potential risk of either food poisoning or food spoilage. Detection of these contaminants using fast and efficient methods would not only prevent outbreaks and recalls but will also increase consumer acceptance and demand for shelf‐stable food products. The conventional culture‐based methods for microbial detection are time and labor‐intensive, whereas hyperspectral imaging (HSI) is robust, nondestructive with minimum sample preparation, and has gained significant attention due to its rapid approach to detection of microbial contaminants. This review is a comprehensive summary of the detection of bacterial, viral, and fungal contaminants in food with detailed emphasis on the specific modeling and datamining approaches used to overcome the specific challenges associated with background and data complexity. [ABSTRACT FROM AUTHOR]

Weeds can be major environmental and economic burdens in New Zealand. Traditional methods of weed control including manual and chemical approaches can be time consuming and costly. Some chemical herbicides may have negative environmental and human health impacts. One of the proposed important steps for providing alternatives to these traditional approaches is the automated identification and mapping of weeds. We used hyperspectral imaging data and machine learning to explore the possibility of fast, accurate and automated discrimination of weeds in pastures where ryegrass and clovers are the sown species. Hyperspectral images from two grasses (Setaria pumila [yellow bristle grass] and Stipa arundinacea [wind grass]) and two broad leaf weed species (Ranunculus acris [giant buttercup] and Cirsium arvense [Californian thistle]) were acquired and pre-processed using the standard normal variate method. We trained three classification models, namely partial least squares-discriminant analysis, support vector machine, and Multilayer Perceptron (MLP) using whole plant averaged (Av) spectra and superpixels (Sp) averaged spectra from each weed sample. All three classification models showed repeatable identification of four weeds using both Av and Sp spectra with a range of overall accuracy of 70–100%. However, MLP based on the Sp method produced the most reliable and robust prediction result (89.1% accuracy). Four significant spectral regions were found as highly informative for characterizing the four weed species and could form the basis for a rapid and efficient methodology for identifying weeds in ryegrass/clover pastures. [ABSTRACT FROM AUTHOR]

Spectroscopy in the visible near-infrared spectral (Vis-NIRS) range combined with imaging techniques (hyperspectral imaging, HSI) allows assessment of chemical composition, texture, and meat structure. The use of HSI in the meat and food industry has observed a significant growth in the last decade, yet its use for assessment of meat it is not optimal yet. The application of HSI for assessment of meat is reviewed with focus on its ability to capture meat unique chemical and structural characteristics. While HSI is widely used for assessment of chemical composition, a limited number of evidences on its ability to handle the effect of different sources of variation on the assessment is found. The use of spatially resolved spectroscopy has been able to detect structural information related to animal background, muscle type, rigor process and ageing. Similarly the use of texture features seem to capture unique characteristics of meat. [ABSTRACT FROM AUTHOR]

Confinement odour was investigated. Volatiles were extracted directly from the pack, using solid phase microextraction and analysed by gas chromatography–mass spectrometry. Sensory evaluation and microbiological analysis of the meat surface were also performed. Commercial samples of vacuum packed lamb legs (n = 85), from two meat processing plants, were kept for 7 weeks at − 1.5 °C then at different regimes of temperature (− 1.5 to + 4 °C) until 11, 12 or 13 weeks. Persistent odour was observed in 66% of samples, confinement odour in 24% and no odour in 11%. Volatiles associated with confinement odour (3-methyl-butanal, 3-hydroxy-2-butanone and sulphur dioxide) corresponded with end/sub products of glucose fermentation and catabolism of amino acids by bacteria (all bacteria naturally found in meat and do not represent a risk to health). Confinement odour could indicate a stage at which the environment for bacteria growth is becoming favourable for the production of volatiles with strong odours that are noticed by the consumer. [ABSTRACT FROM AUTHOR]

Abstract: Prediction of ultimate pH (measured 48h post mortem; pHu) in beef from Visible–near infrared (VIS–NIR) spectra collected 20 to 40min post mortem was assessed. Spectra were collected from carcasses (cows: n=86, bulls: n=170, steers: n=363, and heifers: n=38) in a commercial hot boning abattoir under routine conditions. Partial Least Squares (PLS) models showed limited accuracy with RMSE for validation equal to 0.26, 0.20 and 0.36 for the All-animals, Non-bulls and Bulls models, respectively. The pHu–PLS-predicted values were used to segregate carcasses as normal (pHu <5.8) or high (pHu ≥5.8) showing better performance, by correctly classifying at least 90% of high pHu carcasses. The Non-bulls model was equivalent to the current technology used in the abattoir to classify carcasses based on pHu. Thus near infrared spectroscopy (NIRS) could be used for on-line classification of beef carcasses based on pHu. [Copyright &y& Elsevier]

Abstract: Emulsion and suspension polymerizations are important industrial processes for polymer production. The end-user properties of polymers depend strongly on how the polymerization reactions proceed in time (i.e. a batch or semicontinuous, rate of reagents feeding, etc.). In other words, these reactions are process dependent, which makes the successful process control a key point to ensure high-quality products. In several process control strategies the on-line monitoring of reaction performance is required. Due to the multiphase nature of the emulsion and suspension processes, there is a lack of sensors to perform successful on-line monitoring. Near infrared and Raman spectroscopies have been pointed out as useful approaches for monitoring emulsion and suspension polymerizations and several applications have been described. In such instance, the chemometric approach on relating near infrared and Raman spectra to polymer properties is widely used and has proven to be useful. Nevertheless, the multiphase nature of emulsion and suspension polymerizations also represents a challenge for the chemometric approach based on multivariate calibration models and demands the development of new methods. In this work, a set novel results is presented from the monitoring of 15 batch emulsion reactions that show the chemometric challenge to be faced on development of new methods for successful monitoring of processes taken under dispersed medium. In order to discuss these results, several chemometric approaches were revised. It is shown that Raman and NIR spectroscopic techniques are suitable for on-line monitoring of monomer concentration and polymer content during the polymerizations, as well as medium heterogeneity properties, i.e. average particle size. It is also shown that Hotteling and Q statistics, widely used in chemometrics, might fail in monitoring these reactions, while an approach based on principal curves is able to overcome such restriction. [Copyright &y& Elsevier]

In the meat industry, ensuring product authenticity is important due to its impact on human diet, processing chain as well as for ensuring fair trade practices. This study aims at investigating the potential of snapshot hyperspectral imaging (HSI) and deep learning approach for red-meat classification by combining the spectral and spatial features of HSI data of red-meat products. Moreover, this study provides a comprehensive comparison between snapshot HSI and a standard line-scanning HSI system. A novel deep 3D convolution neural network (3D-CNN) model is proposed for extracting the combined features and then classifying the meat in the HSI image. An innovative graph-based post-processing method is also proposed for enhancing the prediction of the 3D-CNN approach. Results show that the 3D-CNN approach significantly enhanced the overall accuracy of state-of-the-art models. Despite limitations in the spectral information from snapshot HSI, the 3D-CNN model shows robustness in classifying red-meat with an overall accuracy of 96.9% and 97.1% for Near-Infrared (NIR) and visible (VIS) snapshot HSI, respectively. A comparison between the HSI systems revealed that state-of-the-art models are insufficient for achieving accurate classification with snapshot HSI data while the 3D-CNN achieves excellent classification accuracy on all systems by utilizing the whole image information. The current study opens the door for further research towards real-time red-meat authenticity and mobile HSI systems using snapshot HSI and deep learning models. • Snapshot HSI systems collect images at video rate and completely portable systems. • Deep learning provides a deep understanding of spectral-spatial features of meat. • 3D-CNN networks capture the interaction between the chemical and textural of meat. • Deep 3D-CNN provides a stable accuracy on line-scanning and snapshot HSI images. • Machine learning models were highly affected by the spectral range and resolution. [ABSTRACT FROM AUTHOR]

The model food in this study known as mashed potato consisted of ribose (1.0%) and lysine (0.5%) to induce browning via Maillard reaction products. Mashed potato was processed by Coaxially Induced Microwave Pasteurization and Sterilization (CiMPAS) regime to generate an F0 of 6–8 min and analysis of the post-processed food was done in two ways, which included by measuring the color changes and using hyperspectral data acquisition. For visualizing the spectra of each tray in comparison with the control sample (raw mashed-potato), the mean spectrum (i.e., mean of region of interest) of each tray, as well as the control sample, was extracted and then fed to the fitted principal component analysis model and the results coincided with those post hoc analysis of the average reflectance values. Despite the presence of a visual difference in browning, the Lightness (L) values were not significantly (p < 0.05) different to detect a cold spot among a range of 12 processed samples. At the same time, hyperspectral imaging could identify the colder trays among the 12 samples from one batch of microwave sterilization. [ABSTRACT FROM AUTHOR]

Despite various direct transmethylation methods having been published and applied to analysis of meat fatty acid (FA) composition, there are still conflicting ideas about the best method for overcoming all the difficulties posed by analysis of complex mixtures of FA in meat. This study performed a systematic investigation of factors affecting a one-step method for quantitative analysis of fatty acids in freeze-dried animal tissue. Approximately 280 reactions, selected using factorial design, were performed to investigate the effect of temperature, reaction time, acid concentration, solvent volume, sample weight and sample moisture. The reaction yield for different types of fatty acids, including saturated, unsaturated (cis, trans and conjugated) and long-chain polyunsaturated fatty acids was determined. The optimised condition for one-step transmethylation was attained with four millilitres 5% sulfuric acid in methanol (as acid catalyst), four millilitres toluene (as co-solvent), 300 mg of freeze-dried meat and incubation at 70 °C for 2 h, with interim mixing by inversion at 30, 60 and 90 min for 15 s. The optimised condition was applied to meat samples from different species, covering a broad range of fat content and offers a simplified and reliable method for analysis of fatty acids from meat samples. [ABSTRACT FROM AUTHOR]

[Display omitted] • Dead and live Clostridium sporogenes spores were identified using HSI. • Multivariate analysis was successful in quantification the spores on culture media. • 1D-CNN approach was successful in quantification of spores on mashed potato. • Dead spores in samples interfered with classification accuracy of 1D-CNN approach. Clostridium sporogenes spores are used as surrogates for Clostridium botulinum , to verify thermal exposure and lethality in sterilization regimes by food industries. Conventional methods to detect spores are time-consuming and labour intensive. The objectives of this study were to evaluate the feasibility of using hyperspectral imaging (HSI) and deep learning approaches, firstly to identify dead and live forms of C. sporogenes spores and secondly, to estimate the concentration of spores on culture media plates and ready-to-eat mashed potato (food matrix). C. sporogenes spores were inoculated by either spread plating or drop plating on sheep blood agar (SBA) and tryptic soy agar (TSA) plates and by spread plating on the surface of mashed potato. Reflectance in the spectral range of 547–1701 nm from the region of interest was used for principal component analysis (PCA). PCA was successful in distinguishing dead and live spores and different levels of inoculum (102 to 106 CFU/ml) on both TSA and SBA plates, however, was not efficient on the mashed potato (food matrix). Hence, deep learning classification frameworks namely 1D- convolutional neural networks (CNN) and random forest (RF) model were used. CNN model outperformed the RF model and the accuracy for quantification of spores was improved by 4% and 8% in the presence and absence, respectively of dead spores. The screening system used in this study was a combination of HSI and deep learning modelling, which resulted in an overall accuracy of 90–94% when the dead/inactivated spores were present and absent, respectively. The only discrepancy detected was during the prediction of samples with low inoculum levels (<102 CFU/ml). In summary, it was evident that HSI in combination with a deep learning approach showed immense potential as a tool to detect and quantify spores on nutrient media as well as on specific food matrix (mashed potato). However, the presence of dead spores in any sample is postulated to affect the accuracy and would need replicates and confirmatory assays. [ABSTRACT FROM AUTHOR]

Raw bovine milk was heated using common industrial heat treatment conditions to determine how treatment conditions modify proteins and lipids. Processing temperature and time were found to affect both the degree and type of molecular modification. An early and consistent protein marker of heating was identified, namely the presence of the Maillard modification (carboxymethyl or carboxyethyl) at the Lys22 residue in the whey protein glycosylation-dependent cell adhesion molecule 1 (GLCM1). Observable chemical protein modifications generally initially increased with increasing temperature but decreased then under more extreme conditions. For lipids, the concentrations of free fatty acids, methylketones, and oxidised fatty acids were directly correlated with pasteurisation temperature. However, mirroring the trend for protein modification, phosphatidylcholine hydroperoxide levels first increased and then decreased at higher temperature. These protein and lipid modifications are potential markers of milk modification during processing and product development. [ABSTRACT FROM AUTHOR]

Metabolomics has been used for the analysis of meat samples for different applications. Using drip as a proxy for meat could offer an easy and non-invasive way of sampling meat, yielding a homogenous liquid sample easy to prepare for metabolomics analysis. There is currently no standard method for the preparation of drip samples for quantitative metabolomics. The aim of this study was to evaluate six different sample preparation methods for quantitative Nuclear Magnetic Resonance (NMR) metabolomics analysis of drip from a lamb leg with extended shelf life: centrifugation, ultrafiltration, and solvent precipitation using four different solvents or solvent mixtures. The six methods were evaluated based on protein removal efficiency, ability to quantify metabolites, metabolite concentrations, reproducibility, speed and relative cost. Three methods (ultrafiltration, solvent precipitation with either acetonitrile/acetone/methanol or chloroform/methanol) resulted in excellent protein removal, high concentrations of metabolites and high reproducibility and are therefore recommended for preparation of extended shelf life lamb leg drip samples for NMR metabolomics. • Six methods for preparing lamb drip for 1H NMR spectroscopy were evaluated. • Two solvent precipitation methods and ultrafiltration are recommended. • Centrifugation resulted in insufficient protein removal and is not recommended. [ABSTRACT FROM AUTHOR]

This study aimed to determine the profiles of water-soluble metabolites in lamb drip and meat by Nuclear Magnetic Resonance (NMR) spectroscopy, in order to better understand the confinement odour (CO) phenomenon in lamb meat on a molecular level. Thirty-five lamb legs were obtained from two New Zealand meat processing plants and stored for 11 to 13 weeks at temperatures ranging from −1.5 °C to +4.0 °C. A sensorial test classified meat samples as having CO, no odour (NO) or persistent odour (PO). Sixty-three and sixty-two metabolites were identified and quantified in drip and meat samples, respectively. Partial least squares canonical analysis (PLS-CA) showed that CO was correlated with meat and drip metabolites tyramine, formate, alanine, carnosine, urea, proline, aspartate, glutathione and nicotinate. CO was also positively associated with appearance and bloom, but not directly associated with pH, size of the bacterial population or with processing plant. Metabolites associated with CO/PO are substrates or products of glucose fermentation and amino acid catabolism. [ABSTRACT FROM AUTHOR]

Metabolite profiles of chicken breast extracts and water mobility in breasts were studied using proton nuclear magnetic resonance ( 1 H-NMR) spectroscopy and time-domain NMR (TD-NMR) relaxometry, respectively, using normal breast (NB), and wooden breast (WB) and white striping (WS) myopathies in broilers. One thousand eight hundred sixty broilers were raised to commercial standards, receiving the same diets that were formulated as per the different growth stages. At 49 D of age, 200 animals were slaughtered following routine commercial procedures, and at 4 h postmortem, the whole breast (pectoralis major muscle) was removed and visually inspected by an experienced meat inspector who selected NB (without myopathies) and samples with the presence of WS and WB myopathies. Fifteen breasts (5 each of NB, WS, and WB) were analyzed through TD-NMR relaxometry, and samples of approximately 20 g were taken from each breast and frozen at -80°C for metabolite profiling through 1 H-NMR spectroscopy. Multivariate statistical analysis was used to evaluate the effect on water relaxometry and metabolite profile in accordance with the presence and type of myopathy in the breast. 1 H-NMR data showed that the metabolite profiles in WS and WB breasts were different from each other and from NB. This pilot study shows that myopathies appear to be related to hypoxia, connective tissue deposition, lower mitochondrial function, and greater oxidative stress compared with NB. The longitudinal and transverse relaxation time of the breasts determined by TD-NMR relaxometry was shorter for NB than that for WS and WB, indicating greater water mobility in breasts affected by myopathies. 1 H-NMR spectroscopy can be used to differentiate the metabolism of WS, WB, and NB, and TD-NMR has the potential to be a fast, simple, and noninvasive method to distinguish NB from WB and WS. As a practical application, the metabolomic profile as per the occurrence of breast myopathies may be used for a better understanding of these issues, which opens a gap to mitigate the incidence and severity of WS and WB. In addition, the present study brings an opportunity for the development of a new and objective tool to classify the incidence of breast myopathies through TD-NMR relaxometry., (Copyright © 2020. Published by Elsevier Inc.)

This study investigated the feasibility of using hyperspectral imaging (HSI) to characterize the diffusion of acid and water within food structures during gastric digestion. Two different sweet potatoes (steamed and fried) and egg white gel (pH5 and pH9 EWGs) structures were exposed to in vitro gastric digestion before scanning by HSI. Afterward, the moisture or acid present in the digested sample was analyzed for calibration purposes. Calibration models were subsequently built using partial least-squares (PLS). The PLS models indicated that the full-wavelength spectral range (550-1700 nm) had a good ability to predict the spatial distribution of acid ( R cal 2 > 0.82) and moisture ( R cal 2 > 0.88). The spatiotemporal distributions of moisture and acid were mapped across the digested food, and they were shown to depend on the food composition and structure. The kinetic data revealed that the acid and moisture uptakes are governed by Fickian diffusion or by both diffusion and erosion-controlled mechanisms.

The effects of fiber orientation on vis/NIR light propagation were studied in three bovine muscles: biceps brachii, brachialis and soleus. Broadband light was focused onto the sample and the diffuse reflectance spot was captured using a hyperspectral camera (470-1620 nm), after which rhombuses were fitted to equi-intensity points. In samples with fibers running parallel to the measurement surface, the rhombus' major axis was oriented perpendicular to the fiber direction close to the point of illumination. However, at larger distances from the illumination spot, the major axis orientation aligned with the fiber direction. This phenomenon was found to be muscle dependent. Furthermore, the rhombus orientation was highly dependent on the sample positioning underneath the camera, especially when the muscle fibers ran parallel to the measurement surface. The bias parameter, indicating the deviation from a circular shape, was higher for samples with the fibers running parallel to the measurement surface. Moreover, clear effects of wavelength and distance from the illumination point on this parameter were observed. These results show the importance of fiber orientation when considering optical techniques for measurements on anisotropic, fibrous tissues. Moreover, the prediction of muscle fiber orientation seemed feasible, which can be of interest to the meat industry.