Estimation of adenosine triphosphate content in ready-to-eat sausages with different storage days, using hyperspectral imaging coupled with R statistics

Assessment of adenosine triphosphate content in sausages stuffed in different modified casing treatments added with orange extracts, utilising hyperspectral imaging combined with multivariate analysis

Introduction

An investigation was conducted using a hyperspectral imaging (HSI) system to non-invasively estimate adenosine triphosphate (ATP) content in vacuum packaged sausages in different modified casing treatments added with orange extracts after a year of storage at 4°C.

Methods

Various pre-processing combinations were applied to the spectra to enhance the performance of partial least squares regression (PLSR).

Results and discussion

PLSR models, utilising the full absorbance spectrum with pre-treatment of standard normal variate combined with 1st derivative,exhibited prediction coefficients of determination (Rp2) reaching up to 0.6629. A distribution map developed through MATLAB was employed to display the location and concentration of ATP content in these unique sausages for the first time. The integration of HSI and multivariate analysis not only quantifies but also visually represents the changes in ATP content response to the different casing treatments, demonstrating the significant potential for real-time inspection in the processed meat industry.

Simulation of the fluid dynamic and thermal behavior of an experimental passive cooling system of photovoltaic panels

Solar energy capacity has increased significantly globally, with values above 800 GW produced by different systems. Among these, PVT panels can generate either electricity, heat, or both. As these systems present various issues associated with excessive temperature increases, cooling systems have been developed to control the temperature using fluids such as water. The article uses previous data from the Technologic Institute of Sonora, which analyzed various cooling device configurations and selected the best two options (B1 and B4) based on the panel efficiency. Using the boundary conditions and the predicted streamlines, a simulation was made in CFD programs, determining the correct parameters to replicate the system fluid dynamics. Several simulations were carried out using different turbulence models. After comparing the temperature contour diagram and the streamline, it was obtained that the k-ω turbulence model best describes the fluid's behavior. The transient analysis simulations allow us to determine that the B1 configuration delivers the best cooling effect as it presents the most homogeneous temperature profile. BIAS and RMSE were calculated to validate and contrast the results obtained experimentally, obtaining values of 0.8675 and 1.8981, respectively.

Hyperspectral Imaging Combined with Chemometrics Analysis for Monitoring the Textural Properties of Modified Casing Sausages with Differentiated Additions of Orange Extracts

The textural properties (hardness, springiness, gumminess, and adhesion) of 16-day stored sausages with different additions of orange extracts to the modified casing solution were estimated by response surface methodology (RSM) and a hyperspectral imaging system in the spectral range of 390-1100 nm. To improve the model performance, normalization, 1st derivative, 2nd derivative, standard normal variate (SNV), and multiplicative scatter correction (MSC) were applied for spectral pre-treatments. The raw, pretreated spectral data and textural attributes were fit to the partial least squares regression model. The RSM results show that the highest R² value achieved at adhesion (77.57%) derived from a second-order polynomial model, and the interactive effects of soy lecithin and orange extracts on adhesion were significant (p < 0.05). The adhesion of the PLSR model developed from reflectance after SNV pretreatment possessed a higher calibration coefficient of determination (0.8744) than raw data (0.8591). The selected ten important wavelengths for gumminess and adhesion can simplify the model and can be used for convenient industrial applications.

Evaluation of pH in Sausages Stuffed in a Modified Casing with Orange Extracts by Hyperspectral Imaging Coupled with Response Surface Methodology

The pH values of sausages stuffed in natural hog casings with different modifications (soy lecithin, soy oil, orange extracts (OE) from waste orange peels, lactic acid in slush salt, and treatment time) after 16-day 4 °C storage were evaluated for the first time by hyperspectral imaging (350−1100 nm) coupled with response surface methodology (RSM). A partial least squares regression (PLSR) model was developed to relate the spectra to the pH of sausages. Spectral pretreatment, including first derivative, second derivative, multiplicative scatter correction (MSC), standard normal variate (SNV), normalization, and normalization, with different combinations was employed to improve model performance. RSM showed that only soy lecithin and OE interactively affected the pH of sausages (p < 0.05). The pH value decreased when the casing was treated with a higher concentration of soy lecithin with 0.26% OE. As the first and second derivatives are commonly used to eliminate the baseline shift, the PLSR model derived from absorbance pretreated by the first derivative in the full wavelengths showed a calibration coefficient of determination (R2) of 0.73 with a root mean square error of calibration of 0.4283. Twelve feature wavelengths were selected with a comparable R2 value compared with the full wavelengths. The prediction map enables the visualization of the pH evolution of sausages stuffed in the modified casings added with OE.

Advances in Machine Learning and Hyperspectral Imaging in the Food Supply Chain

Food quality and safety are the essential hot issues of social concern. In recent years, there has been a growing demand for real-time food information, and non-destructive testing is gradually replacing traditional manual sensory testing and chemical analysis methods with lagging and destructive effects and has strong potential for application in the food supply chain. With the maturity and development of computer science and spectroscopic techniques, machine learning and hyperspectral imaging (HSI) have been widely demonstrated as efficient detection techniques that can be applied to rapidly evaluate sensory characteristics and quality attributes of food products nondestructively and efficiently. This paper first briefly described the basic concepts of hyperspectral imaging and machine learning, including the imaging process of HSI, the type of algorithms contained in machine learning, and the data processing flow. Secondly, this paper provided an objective and comprehensive overview of the current applications of machine learning and HSI in the food supply chain for sorting, packaging, transportation, storage, and sales, based on the state-of-art literature from 2017 to 2022. Finally, the potential of the technology is further discussed to provide optimized ideas for practical application.

Hyperspectral imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends

Meat products are particularly plagued by safety problems because of their complicated structure, various production processes and complex supply chains. Rapid and non-invasive analytical methods to evaluate meat quality have become a priority for the industry over the conventional chemical methods. To achieve rapid analysis of safety and quality parameters of meat products, hyperspectral imaging (HSI) is now widely applied in research studies for detecting the various components of different meat products, but its application in meat production and supply chain integrity as a quality control (QC) solution is still ambiguous. This review presents the fresh look at the current states of HSI research as both the scope and the applicability of the HSI in the meat quality evaluation expanded. The future application scenarios of HSI in the supply chain and the future development of HSI hardware and software are also discussed, by which HSI technology has the potential to enable large scale meat product testing. With a fully adapted for factory setting HSI, the inspection coverage can reliably identify the chemical properties of meat products. With the introduction of Food Industry 4.0, HSI advances can change the meat industry to become from reactive to predictive when facing meat safety issues.

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HSI has shown promising early signs in the non-destructive analysis of meat quality and safety.

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Hyperspectral imaging (HSI) is now widely applied in research studies for different meat products with the help of machine learning methods.

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With a fully adapted factory setting and robust machine learning of HSI, the inspection coverage can reach 100% of the target meat.

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HSI can change the meat industry to become from reactive to predictive when facing issues, this will be translated into fewer recalls, less meat fraud, and less waste.

Optimizing Procedures of Ultrasound-Assisted Extraction of Waste Orange Peels by Response Surface Methodology

The simultaneous effects of three continuous factors: solvent concentration (50−100%), treated times (25−85 min), treated temperatures (25−55 °C), and two categorical factors: type of solvents (methanol or ethanol) and ultrasonic frequency (28 kHz or 40 kHz) on ultrasonic-assisted extraction yield from waste orange peels were evaluated and optimized by response surface methodology. Fourier Transform Infrared (FTIR) spectroscopy with a wavelength of 500 cm−1 to 4000 cm−1 was employed to rapidly identify the orange extracts. The significant polynomial regression models on crude extraction, sediments after evaporation, and precipitation yield were established (p < 0.05). Results revealed that solvent concentration affected crude extraction and precipitation yield linearly (p < 0.01). The optimal and practical ultrasound-assisted extraction conditions for increasing the precipitation yield were using 61.42% methanol with 85 min at 55 °C under 40 kHz ultrasonic frequency. The spectra of extracts showed a similar fingerprint of hesperidin.

Quality Evaluation and Mathematical Modelling Approach to Estimate the Growth Parameters of Total Viable Count in Sausages with Different Casings

The growth kinetics for the total viable count (TVC) in sausages with modified hog casings (treated by surfactant solutions and slush salt with lactic acid), natural hog casings and sheep casings as a function of the storage time (up to 50 days) were studied for the first time. The growth of TVC was fitted by the Baranyi model, and the maximum specific growth rate, lag time and initial and final cell populations were estimated via DMFit. The coefficient of determination of the Baranyi model reached 0.94, 0.77 and 0.86 for sausages stuffed in modified hog casings (MHC), control hog casings (CHC) and natural sheep casings (NSC), respectively. The experimental data for the initial populations were 4.69 ± 0.10 log cfu/g for MHC, 4.79 ± 0.10 log cfu/g for CHC and 3.74 ± 0.14 log cfu/g for NSC, whilst the predicted initial cell populations for MHC, CHC and NSC were 4.81 ± 0.20 log cfu/g, 5.19 ± 0.53 log cfu/g and 3.74 ± 0.54 log cfu/g, respectively. Their shelf lives can also be predicted. The results show that the average pH value of MHC samples (6.96 ± 0.01) was significantly lower than that of CHC (7.09 ± 0.01) and NSC (7.05 ± 0.02) samples at day 50 (p < 0.05). Sausages with CHC possessed a significant higher water holding capacity (99.48 ± 0.14%) at d 29 than those with MHC (97.40 ± 0.46%) and NSC (98.55 ± 0.17%) (p < 0.05). On the last day, the average moisture content for samples with NSC (38.30 ± 3.23%) was significantly higher than that for those with MHC (29.38 ± 2.52%) and CHC (29.15 ± 1.16%) (p < 0.05).

Simulation of Thermal and Electric Field Distribution in Packaged Sausages Heated in a Stationary Versus a Rotating Microwave Oven

The microwave oven has become a standard appliance to reheat or cook meals in households and convenience stores. However, the main problem of microwave heating is the non-uniform temperature distribution, which may affect food quality and health safety. A three-dimensional mathematical model was developed to simulate the temperature distribution of four ready-to-eat sausages in a plastic package in a stationary versus a rotating microwave oven, and the model was validated experimentally. COMSOL software was applied to predict sausage temperatures at different orientations for the stationary microwave model, whereas COMSOL and COMSOL in combination with MATLAB software were used for a rotating microwave model. A sausage orientation at 135° with the waveguide was similar to that using the rotating microwave model regarding uniform thermal and electric field distributions. Both rotating models provided good agreement between the predicted and actual values and had greater precision than the stationary model. In addition, the computational time using COMSOL in combination with MATLAB was reduced by 60% compared to COMSOL alone. Consequently, the models could assist food producers and associations in designing packaging materials to prevent leakage of the packaging compound, developing new products and applications to improve product heating uniformity, and reducing the cost and time of the research and development stage.

Hyperspectral imaging technology for monitoring of moisture contents of dried persimmons during drying process

The moisture content of persimmons during drying was monitored by hyperspectral imaging technology. All persimmons were dried using a hot-air dryer at 40 °C and divided into seven groups according to drying time: semi-dried persimmons (Cont), 1 day (DP-1), 2 days (DP-2), 3 days (DP-3), 4 days (DP-4), 5 days (DP-5), and 6 days (DP-6). Shortwave infrared hyperspectral spectra and moisture content of all persimmons were analyzed to develop a prediction model using partial least squares regression. There were obvious absorption bands: two at approximately 971 nm and 1452 nm were due to water absorption related to O-H stretching of the second and first overtones, respectively. The R-squared value of the optimal calibration model was 0.9673, and the accuracy of the moisture content measurement was 95%. These results indicate that hyperspectral imaging technology can be used to predict and monitor the moisture content of dried persimmons during drying.

Hyperspectral Imaging Coupled with Multivariate Analysis and Image Processing for Detection and Visualisation of Colour in Cooked Sausages Stuffed in Different Modified Casings

A hyperspectral imaging system was for the first time exploited to estimate the core colour of sausages stuffed in natural hog casings or in two hog casings treated with solutions containing surfactants and lactic acid in slush salt. Yellowness of sausages stuffed in natural hog casings (control group, 20.26 ± 4.81) was significantly higher than that of sausages stuffed in casings modified by submersion for 90 min in a solution containing 1:30 (w/w) soy lecithin:distilled water, 2.5% wt. soy oil, and 21 mL lactic acid per kg NaCl (17.66 ± 2.89) (p < 0.05). When predicting the lightness and redness of the sausage core, a partial least squares regression model developed from spectra pre-treated with a second derivative showed calibration coefficients of determination (R_c²) of 0.73 and 0.76, respectively. Ten, ten, and seven wavelengths were selected as the important optimal wavelengths for lightness, redness, and yellowness, respectively. Those wavelengths provide meaningful information for developing a simple, cost-effective multispectral system to rapidly differentiate sausages based on their core colour. According to the canonical discriminant analysis, lightness possessed the highest discriminant power with which to differentiate sausages stuffed in different casings.

Terahertz spectroscopy technology as an innovative technique for food: Current state-of-the-Art research advances

With the dramatic development of source and detector components, terahertz (THz) spectroscopy technology has recently shown a renaissance in various fields such as medical, material, biosensing and pharmaceutical industry. As a rapid and noninvasive technology, it has been extensively exploited to evaluate food quality and ensure food safety. In this review, the principles and processes of THz spectroscopy are first discussed. The current state-of-the-art applications of THz and imaging technologies focused on foodstuffs are then discussed. The advantages and challenges are also covered. This review offers detailed information for recent efforts dedicated to THz for monitoring the quality and safety of various food commodities and the feasibility of its widespread application. THz technology, as an emerging and unique method, is potentially applied for detecting food processing and maintaining quality and safety.

Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks

Developing a noninvasive technique to estimate the degreening (loss of green color) velocity of harvested broccoli was attempted. Loss of green color on a harvested broccoli head occurs heterogeneously. Therefore, hyperspectral imaging technique that stores spectral reflectance with spatial information was used in the present research. Using artificial neural networks (ANNs), we demonstrated that the reduction velocity of chlorophyll at a site on a broccoli head was related to the second derivative of spectral reflectance data at 15 wavelengths from 405 to 960 nm. The reduction velocity was predicted using the ANNs model with a correlative coefficient of 0.995 and a standard error of prediction of 5.37 × 10^-5 mg·g^-1·d^-1. The estimated reduction velocity was effective for predicting the chlorophyll concentration of broccoli buds until 7 d of storage, which was established as the maximum time for maintaining marketability. This technique may be useful for nondestructive prediction of the shelf life of broccoli heads.

A KPI-Based Probabilistic Soft Sensor Development Approach that Maximizes the Coefficient of Determination

Advanced technology for process monitoring and fault diagnosis is widely used in complex industrial processes. An important issue that needs to be considered is the ability to monitor key performance indicators (KPIs), which often cannot be measured sufficiently quickly or accurately. This paper proposes a data-driven approach based on maximizing the coefficient of determination for probabilistic soft sensor development when data are missing. Firstly, the problem of missing data in the training sample set is solved using the expectation maximization (EM) algorithm. Then, by maximizing the coefficient of determination, a probability model between secondary variables and the KPIs is developed. Finally, a Gaussian mixture model (GMM) is used to estimate the joint probability distribution in the probabilistic soft sensor model, whose parameters are estimated using the EM algorithm. An experimental case study on the alumina concentration in the aluminum electrolysis industry is investigated to demonstrate the advantages and the performance of the proposed approach.