Classification of dry-cured hams according to the maturation time using near infrared spectra and artificial neural networks

Near-Infrared Spectroscopy Procedure for Online Determination of Sodium and Potassium Content in Low-Salt Cured Hams

This paper reports the development of a near-infrared spectroscopy (NIRS) calibration procedure for the determination of sodium and potassium content in cured ham samples. Sliced samples of hams treated with different salts in different percentages were included in the study. Calibration models developed using partial least squares regression were cross-validated and predictive models were tested using the samples of cured ham with low sodium content. The results showed that the developed NIRS procedure is capable of directly measuring the potassium content of packaged dry-cured ham slices with low sodium content with a fitting accuracy of 91.44%, and that it can indirectly determine the sodium content by applying a correction factor to the values obtained for potassium. The prediction error between the calculated and actual sodium values determined using inductively coupled plasma atomic emission spectrophotometry (ICP-AES) was 0.004%, and this confirms that the NIRS procedure is a viable option for the determination of sodium and potassium content in this type of sample.

A quantitative on-line analysis of salt in cured ham by near-infrared spectroscopy and chemometrics

In this work, non-invasive near-infrared spectroscopy (NIRS) combined with chemometrics was evaluated as a possible online analytical technique to categorize pieces of cured ham on the industrial production line based on their maximum sodium content. Stifle muscle was selected for the development of the NIRS prediction models because it is the one with the highest sodium content and the easiest in terms of accessibility for spectral measurement. In the study, samples with varying thicknesses were taken. The suitability of this method is demonstrated when a 5 mm sample is used for the construction of the model, obtaining the best fit with an R²cv of 92% and a prediction error of 0.11% sodium that coincides with the error of the reference method. In conclusion, a method is proposed for the direct determination of sodium content on the production line which allows the different pieces of ham to be quickly categorized according to their salt content.

Comparison of Nutritional Profiles of Super Worm (Zophobas morio) and Yellow Mealworm (Tenebrio molitor) as Alternative Feeds Used in Animal Husbandry: Is Super Worm Superior?

Edible insects are acknowledged as a valuable nutritional source and promising alternative to traditional feed ingredients, while the optimization of rearing conditions is required for their wider utilization in the animal feed industry. The main goal of this study was to compare and optimize the rearing conditions of the two species’ larvae and identify the most favorable nutritive composition of the full-fat larval meal. For that purpose, Tenebrio molitor (TM) and Zophobas morio (ZM) were reared on three different substrates and harvested after three time periods. An artificial neural network (ANN) with multi-objective optimization (MOO) was used to investigate the influence between the observed parameters as well as to optimize and determine rearing conditions. The optimization of the larval rearing conditions showed that the best nutritive composition of full-fat larval meal was obtained for ZM larvae reared on a mixture of cabbage, carrot and flaxseed and harvested after 104 days. The best nutritive composition contained 39.52% protein, 32% crude fat, 44.01% essential amino acids, 65.21 mg/100 g Ca and 651.15 mg/100 g P with a favorable ratio of 1.5 of n6/n3 fatty acids. Additionally, the incorporation of flaxseed in the larval diet resulted in an increase in C18:3n3 content in all samples.

Infrared Predictions Are a Valuable Alternative to Actual Measures of Dry-Cured Ham Weight Loss in the Training of Genome-Enabled Prediction Models

Selection to reduce ham weight losses during dry-curing (WL) requires individual traceability of hams throughout dry-curing, with high phenotyping costs and long generation intervals. Infrared spectroscopy enables cost-effective, high-throughput phenotyping for WL 24 h after slaughter. Direct genomic values (DGV) of crossbred pigs and their purebred sires were estimated, for observed (OB) and infrared-predicted WL (IR), through models developed from 640 and 956 crossbred pigs, respectively. Five Bayesian models and two pseudo-phenotypes (estimated breeding value, EBV, and adjusted phenotype) were tested in random cross-validation and leave-one-family-out validation. The use of EBV as pseudo-phenotypes resulted in the highest accuracies. Accuracies in leave-one-family-out validation were much lower than those obtained in random cross-validation but still satisfactory and very similar for both traits. For sires in the leave-one-family-out validation scenario, the correlation between the DGV for IR and EBV for OB was slightly lower (0.32) than the correlation between the DGV for OB and EBV for OB (0.38). While genomic prediction of OB and IR can be equally suggested to be incorporated in future selection programs aiming at reducing WL, the use of IR enables an early, cost-effective phenotyping, favoring the construction of larger reference populations, with accuracies comparable to those achievable using OB phenotype.

Prediction of mechanical extraction oil yield of new sunflower hybrids: artificial neural network model

BACKGROUND

Sunflower seeds are in the top five most abundant oilseeds in the world, as well as sunflower oil in the edible oils group. Recently, increasing attention has been paid to cold-pressed sunflower oil because less processing is involved and no solvent is used. The present study was carried out to investigate dimensions (length, width, thickness), firmness, general (moisture content and hull content, mass of 1000 seeds), gravimetric (true and bulk density, porosity) and geometric characteristics (equivalent diameter, surface area, seed volume, sphericity) of 20 new sunflower hybrid seeds. Steps to determine most of these parameters are quite simple and easy since the process does not require long time or special equipment.

RESULTS

Principal component analysis and cluster analysis confirmed differences in the mentioned characteristics between oily and confectionary sunflower hybrid seeds. One of the major differences between two groups of samples was in extraction oil yield. Mechanical extraction oil yield of the oily hybrid seeds was significantly (P ˂ 0.05) higher (from 68.72 ± 4.21% to 75.61 ± 1.99%) compared to confectionary hybrids (from 20.10 ± 2.82% to 39.91 ± 6.23%). Extraction oil yield values are known only after oil extraction.

CONCLUSION

Knowledge of the extraction oil yield value before the mechanical extraction enables better management of the process. By application of the artificial neural network approach, an optimal neural network model was developed. The developed model showed a good generalization capability to predict the mechanical extraction oil yield of new sunflower hybrids based on the experimental data, which was a main goal of this paper. © 2021 Society of Chemical Industry.

Characterization of Subcutaneous Fat of Toscano Dry-Cured Ham and Identification of Processing Stage By Multivariate Analysis Approach Based on Volatile Profile

During ham processing the action of endogenous proteolytic and lipolytic enzymes leads to the development of volatile compounds (VOCs) responsible of typical aromas. Protected Designation of Origin (PDO) of Toscano ham requires at least 12 months of ripening but extended seasoning might improve flavor and economic value. This study aimed at assessing the evolution of color, fatty acids, and VOCs profile in subcutaneous fat, and, among VOCs, at identifying possible markers characterizing different seasoning length. For this purpose, a reduced pool of VOCs was selected by 3 multivariate statistical techniques (stepwise discriminant analysis, canonical discriminant analysis and discriminant analysis) to classify hams according to ripening (<12 months) or seasoning (≥12 months) periods and also to seasoning length (S12, S14, S16, or S18 months). The main VOCs chemical families steadily increased along ripening. Aldehydes and hydrocarbons reached their peaks at S16, acids and ketones remained constant from R6 to S16, whereas esters started decreasing after 12 months of seasoning. Stepwise analysis selected 5 compounds able to discriminate between ripening and seasoning periods, with 1,1-diethoxyhexane and dodecanoic acid being the most powerful descriptors for ripening and seasoning period, respectively. Instead, 12 compounds were needed to correctly classify hams within seasoning. Among them, undecanoic acid methyl ester, formic acid ethyl ester, 2,4,4-trimethylhexane, and 6-methoxy-2-hexanone had a central role in differentiating the seasoning length.

Prediction of Sensory Parameters of Cured Ham: A Study of the Viability of the Use of NIR Spectroscopy and Artificial Neural Networks

Dry-cured ham is a high-quality product owing to its organoleptic characteristics. Sensory analysis is an essential part of assessing its quality. However, sensory assessment is a laborious process which implies the availability of a trained tasting panel. The aim of this study was the prediction of dry-ham sensory characteristics by means of an instrumental technique. To do so, an artificial neural network (ANN) model for the prediction of sensory parameters of dry-cured hams based on NIR spectral information was developed and optimized. The NIR spectra were obtained with a fiber-optic probe applied directly to the ham sample. In order to achieve this objective, the neural network was designed using 28 sensory parameters analyzed by a trained panel for sensory profile analysis as output data. A total of 91 samples of dry-cured ham matured for 24 months were analyzed. The hams corresponded to two different breeds (Iberian and Iberian x Duroc) and two different feeding systems (feeding outdoors with acorns or feeding with concentrates). The training algorithm and ANN architecture (the number of neurons in the hidden layer) used for the training were optimized. The parameters of ANN architecture analyzed have been shown to have an effect on the prediction capacity of the network. The Levenberg–Marquardt training algorithm has been shown to be the most suitable for the application of an ANN to sensory parameters

High-Throughput Phenotyping Approach for Screening Major Carotenoids of Tomato by Handheld Raman Spectroscopy Using Chemometric Methods

Our objective was to develop a rapid technique for the non-invasive profiling and quantification of major tomato carotenoids using handheld Raman spectroscopy combined with pattern recognition techniques. A total of 106 samples with varying carotenoid profiles were provided by the Ohio State University Tomato Breeding and Genetics program and Lipman Family Farms (Naples, FL, USA). Non-destructive measurement from the surface of tomatoes was performed by a handheld Raman spectrometer equipped with a 1064 nm excitation laser, and data analysis was performed using soft independent modelling of class analogy (SIMCA)), artificial neural network (ANN), and partial least squares regression (PLSR) for classification and quantification purposes. High-performance liquid chromatography (HPLC) and UV/visible spectrophotometry were used for profiling and quantification of major carotenoids. Seven groups were identified based on their carotenoid profile, and supervised classification by SIMCA and ANN clustered samples with 93% and 100% accuracy based on a validation test data, respectively. All-trans-lycopene and β-carotene levels were measured with a UV-visible spectrophotometer, and prediction models were developed using PLSR and ANN. Regression models developed with Raman spectra provided excellent prediction performance by ANN (r_pre = 0.9, SEP = 1.1 mg/100 g) and PLSR (r_pre = 0.87, SEP = 2.4 mg/100 g) for non-invasive determination of all-trans-lycopene in fruits. Although the number of samples were limited for β-carotene quantification, PLSR modeling showed promising results (r_cv = 0.99, SECV = 0.28 mg/100 g). Non-destructive evaluation of tomato carotenoids can be useful for tomato breeders as a simple and rapid tool for developing new varieties with novel profiles and for separating orange varieties with distinct carotenoids (high in β-carotene and high in cis-lycopene).

Rapid discrimination of Chinese dry-cured hams based on Tri-step infrared spectroscopy and computer vision technology

The aim of this study was to establish rapid and efficient methods based on a Tri-step infrared spectroscopy (Fourier transform infrared spectroscopy (FT-IR) integrated with second derivative infrared spectroscopy (SD-IR) and two-dimensional correlation infrared spectroscopy (2DCOS-IR)) and computer vision technology to identify and evaluate the quality of three Chinese dry-cured hams (Jinhua, Xuanwei and Rugao hams). 9 dry-cured hams (3 different quality grades of each geographical origin) had similar IR spectra. Nevertheless, they could be further discriminated visually by SD-IR and 2DCOS-IR spectra. All samples can be separated by the computer vision technology incorporated with Principal Component Analysis (PCA) and Cluster analysis (CA). This study not only preliminarily verified the possibility of using Tri-step infrared spectroscopy and computer vision technology to discriminate the geographical origins and quality grades of Chinese dry-cured hams, but also provided prospects of the application of infrared spectroscopy and computer vision technology to authenticate other meat products.

Temperature and pH growth profile prediction of newly isolated bacterial strains from alkaline soils

BACKGROUND

Soil microorganisms can form complex and varied communities which interact with each other in many different ways depending on environmental conditions. These microbial diversities are accompanied by different metabolic paths and adaptability reflected even in extreme environments. In recent decades, the biodiversity of microbes in extreme environments has been in scientific focus because such specifically adapted bacteria can improve bioremediation processes in industrial and agricultural applications. Instead of the time-consuming process of identification of new bacterial strains from habitats rich in microbiota, artificial neural networks have been proposed as a mapping model for resolving the problem of prediction of microbial behaviour.

RESULTS

The occurrence and diversity of alkaliphilic sporogenic bacteria in alkaline soils were investigated. For this purpose, soil samples were collected from various locations: leached soil from the Danube river, cement factory wastewater accumulation, deposit of limestone near the Bešenovo lake and the Beli Majdan cave in the Fruška gora mountain. According to the obtained results, two empirical models were developed that gave a good fit to experimental data and were able to predict successfully the pH and temperature growth profiles of the natural isolates. The artificial neural network models showed a reasonably good predictive capability (overall R² for temperature growth profile was 0.727, while the overall R² for pH growth profile was 0.906).

CONCLUSIONS

The developed mathematical models provided adequate precision for practical study in the microbiology laboratory and scale-up processes for a wide range of laboratory and industrial applications, where specifically adapted microbial communities are needed. © 2019 Society of Chemical Industry.

Computer Vision Classification of Barley Flour Based on Spatial Pyramid Partition Ensemble

Imaging sensors are largely employed in the food processing industry for quality control. Flour from malting barley varieties is a valuable ingredient in the food industry, but its use is restricted due to quality aspects such as color variations and the presence of husk fragments. On the other hand, naked varieties present superior quality with better visual appearance and nutritional composition for human consumption. Computer Vision Systems (CVS) can provide an automatic and precise classification of samples, but identification of grain and flour characteristics require more specialized methods. In this paper, we propose CVS combined with the Spatial Pyramid Partition ensemble (SPPe) technique to distinguish between naked and malting types of twenty-two flour varieties using image features and machine learning. SPPe leverages the analysis of patterns from different spatial regions, providing more reliable classification. Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), J48 decision tree, and Random Forest (RF) were compared for samples' classification. Machine learning algorithms embedded in the CVS were induced based on 55 image features. The results ranged from 75.00% (k-NN) to 100.00% (J48) accuracy, showing that sample assessment by CVS with SPPe was highly accurate, representing a potential technique for automatic barley flour classification.

El Gueddid, a traditional Algerian dried salted meat: Physicochemical, microbiological characteristics and proteolysis intensity during its manufacturing process and ripening

El Gueddid is a traditional salted and dried meat with high popularity in Algeria. It is used as an ingredient in various dishes. In this study, different samples of El Gueddid were analyzed at different processing times to follow up their microbiological and physicochemical properties. Changes in the protein profile were also demonstrated by electrophoretic study of myofibrillar proteins. Microbiological determinations included the total viable count, coliforms, Staphylococci, lactic acid bacteria, yeasts, and molds, whereas physicochemical properties were characterized by pH, moisture, salt content and water activity. The results showed that microbial profiles were elevated for all the studied micro-organisms. Staphylococci and lactic acid bacteria were the most abundant micro-organisms in the product. Total coliforms were found in low numbers in fresh meat, being eliminated at the post salting stage of process. The physicochemical characteristics showed that the moisture content decreased in the product during the drying period. The pH also decreased during the drying period, then remained almost unchanged during the rest of the ripening period. Moreover, El Gueddid showed low water activity and high salt content. One of the most important changes in the profile of myofibrillar proteins was a reduction in the myosin heavy chain content.

Dry-cured ham tissue characterization by fast field cycling NMR relaxometry and quantitative magnetization transfer

Fast field cycling (FFC) and quantitative magnetization transfer (qMT) NMR methods are two powerful tools in NMR analysis of biological tissues. The qMT method is well established in biomedical NMR applications, while the FFC method is often used in investigations of molecular dynamics on which longitudinal NMR relaxation times of the investigated material critically depend. Despite their proven analytical potential, these two methods were rarely used in NMR studies of food, especially when combined together. In our study, we demonstrate the feasibility of a combined FFC/qMT-NMR approach for the fast and nondestructive characterization of dry-curing ham tissues differing by protein content. The characterization is based on quantifying the pure quadrupolar peak area (area under the quadrupolar contribution of dispersion curve obtained by FFC-NMR) and the restricted magnetization pool size (obtained by qMT-NMR). Both quantities correlate well with concentration of partially immobilized, nitrogen-containing and proton magnetization exchanging muscle proteins. Therefore, these two quantities could serve as potential markers for dry-curing process monitoring. Copyright © 2016 John Wiley & Sons, Ltd.