Prediction of quality parameters of food residues using NIR spectroscopy and PLS models based on proximate analysis

Determination of fumonisin content in maize using near-infrared hyperspectral imaging (NIR-HSI) technology and chemometric methods

Maize (Zea mays L.) is of socioeconomic importance as an essential food for human and animal nutrition. However, cereals are susceptible to attack by mycotoxin-producing fungi, which can damage health. The methods most commonly used to detect and quantify mycotoxins are expensive and time-consuming. Therefore, alternative non-destructive methods are required urgently. The present study aimed to use near-infrared spectroscopy with hyperspectral imaging (NIR-HSI) and multivariate image analysis to develop a rapid and accurate method for quantifying fumonisins in whole grains of six naturally contaminated maize cultivars. Fifty-eight samples, each containing 40 grains, were subjected to NIR-HSI. These were subsequently divided into calibration (38 samples) and prediction sets (20 samples) based on the multispectral data obtained. The averaged spectra were subjected to various pre-processing techniques (standard normal variate (SNV), first derivative, or second derivative). The most effective pre-treatment performed on the spectra was SNV. Partial least squares (PLS) models were developed to quantify the fumonisin content. The final model presented a correlation coefficient (R2) of 0.98 and root mean square error of calibration (RMSEC) of 508 µg.kg-1 for the calibration set, an R2 of 0.95 and root mean square error of prediction (RMSEP) of 508 µg.kg-1 for the test validation set and a ratio of performance to deviation of 4.7. It was concluded that NIR-HSI with partial least square regression is a rapid, effective, and non-destructive method to determine the fumonisin content in whole maize grains.

Rice bean-adzuki bean multitrait near infrared reflectance spectroscopy prediction model: a rapid mining tool for trait-specific germplasm

In the present era of climate change, underutilized crops such as rice beans and adzuki beans are gaining prominence to ensure food security due to their inherent potential to withstand extreme conditions and high nutritional value. These legumes are bestowed with higher nutritional attributes such as protein, fiber, vitamins, and minerals than other major legumes of the Vigna family. With the typical nutrient evaluation methods being expensive and time-consuming, non-invasive techniques such as near infrared reflectance spectroscopy (NIRS) combined with chemometrics have emerged as a better alternative. The present study aims to develop a combined NIRS prediction model for rice bean and adzuki bean flour samples to estimate total starch, protein, fat, sugars, phytate, dietary fiber, anthocyanin, minerals, and RGB value. We chose 20 morphometrically diverse accessions in each crop, of which fifteen were selected as the training set and five for validation of the NIRS prediction model. Each trait required a unique combination of derivatives, gaps, smoothening, and scatter correction techniques. The best-fit models were selected based on high RSQ and RPD values. High RSQ values of >0.9 were achieved for most of the studied parameters, indicating high-accuracy models except for minerals, fat, and phenol, which obtained RSQ <0.6 for the validation set. The generated models would facilitate the rapid nutritional exploitation of underutilized pulses such as adzuki and rice beans, showcasing their considerable potential to be functional foods for health promotion.

Combination of gamma irradiation and storage condition for improving mechanical and physical postharvest characteristics of fresh garlic cloves

The aim of this study was the discrimination and optimization of irradiation effect under physical and mechanical experiments on garlic. The samples were irradiated with 0, 75, and 150 Gy doses and stored at 4 and 18°C for 5 months. Physical, mechanical, and color properties were measured in the period of storage. Based on the results, all irradiated garlic samples had less quality variation than control samples. Response surface methodology (RSM) optimized dose, storage time, and temperature of the stored garlic which was 75 Gy, 2 months, and 17°C, respectively. In addition, after finding the optimal dose, time, and temperature, the most effective factor as weight loss was obtained and the data were classified by the principal component analysis (PCA) approach. The results showed that the PCA method had a high ability to classify and separate the data obtained from measuring the physicochemical properties of garlic and cover 99% variance of data. Moreover, partial least square (PLS) was applied for predicting weight loss data with R2 0.9999. As well, a mechanical test was investigated for finding the best situation and duration of storage condition. Finally, irradiation prevented the destruction of garlic and saved garlic in the best quality as compared with control or nonirradiated samples. After all this, it can be decided to keep garlic in warehouses and transfer this product with minimum damage.

Prediction of the proximate analysis parameters of refuse-derived fuel based on deep learning approach

Determination of proximate characteristics can be achieved using conventional analyses methods that require a certain amount of time. In cement factories, refuse-derived fuel (RDF) is continuously fed to a kiln by a conveyor belt, so even if an inappropriate proximate characteristic is determined, it would be too late to prevent the feeding of RDF to the kiln. To overcome this problem, there is a need for instant measurement of the proximate characteristics (moisture, volatile matter, ash) that enables the feeding to be stopped. In such cases, the deep learning (DL) is a useful method based on the prediction of proximate characteristics. Therefore, in this study, the aim is to estimate the mentioned parameters developed by near-infrared spectroscopy (NIR) combined with deep learning models. For this purpose, the spectrographic measurements taken from RDF samples with an NIR spectrometer, and the results of proximate analysis in a laboratory, were used together as a dataset. A fully convolutional neural network (FCNN) and ResNet were used as a network, and they were trained using images of RDF samples and proximate analysis values. The FCNN model was more successful in prediction studies. According to the FCNN model, the results show that the models in the study can predict the moisture, ash, and volatile matter content of RDF with satisfactory R² values between 0.979, 0.983, and 0.952.

Near-Infrared Spectroscopy Coupled with Chemometrics and Artificial Neural Network Modeling for Prediction of Emulsion Droplet Diameters

There is increased interest in the food industry for emulsions as delivery systems to preserve the stability of sensitive biocompounds with the aim of improving their bioavailability, solubility, and stability; maintaining their texture; and controlling their release. Emulsification in continuously operated microscale devices enables the production of emulsions of controllable droplet sizes and reduces the amount of emulsifier and time consumption, while NIR, as a nondestructive, noninvasive, fast, and efficient technique, represents an interesting aspect for emulsion investigation. The aim of this work was to predict the average Feret droplet diameter of oil-in-water and oil-in-aqueous mint extract emulsions prepared in a continuously operated microfluidic device with different emulsifiers (PEG 1500, PEG 6000, and PEG 20,000) based on the combination of near-infrared (NIR) spectra with chemometrics (principal component analysis (PCA) and partial least-squares (PLS) regression) and artificial neural network (ANN) modeling. PCA score plots for average preprocessed NIR spectra show the specific grouping of the samples into three groups according to the emulsifier used, while the PCA analysis of the emulsion samples with different emulsifiers showed the specific grouping of the samples based on the amount of emulsifier used. The developed PLS models had higher R² values for oil-in-water emulsions, ranging from 0.6863 to 0.9692 for calibration, 0.5617 to 0.8740 for validation, and 0.4618 to 0.8692 for prediction, than oil-in-aqueous mint extract emulsions, with R² values that were in range of 0.8109-0.8934 for calibration, 0.5017-0.6620, for validation and 0.5587-0.7234 for prediction. Better results were obtained for the developed nonlinear ANN models, which showed R² values in the range of 0.9428-0.9917 for training, 0.8515-0.9294 for testing, and 0.7377-0.8533 for the validation of oil-in-water emulsions, while for oil-in-aqueous mint extract emulsions R² values were higher, in the range of 0.9516-0.9996 for training, 0.9311-0.9994 for testing, and 0.8113-0.9995 for validation.

Semantic segmentation-based mechanized harvesting soybean quality detection

Crushing rate and impurity rate are important quality indicators of mechanically harvested soybeans. Intelligent quality detection of mechanically harvested soybeans based on machine vision is of great significance to evaluate soybean quality accurately and rapidly. This study proposes an improved U-Net method for identifying intact soybean grains, crushing soybean grains, and impurities. Based on the accurate identification of soybean components and using the quantitative model of soybean crushing rate and impurity rate, the quality of soybean samples can be detected in real-time. To this end, a soybean quality inspection system is designed to realize the dynamic collection and detection of soybean samples. The test results show that the comprehensive evaluation index values of the improved U-Net segmentation algorithm in identifying intact soybean grains, crushing soybean grains, and impurities are 93.04%, 89.40%, and 96.49%, respectively. Compared with the traditional U-Net model, the performance of the indicators is improved by 3.23%, 0.17% and 0.72%, respectively. Compared with manual detection, the maximum absolute error of the crushing rate detection of the soybean quality detection system is 0.57%, and the maximum absolute error of the impurity rate detection is 0.69%. The proposed soybean quality inspection system can be used as an effective tool for real-time online inspection of soybean quality.

Application of NIRs coupled with PLS and ANN modelling to predict average droplet size in oil-in-water emulsions prepared with different microfluidic devices

In this study, the potential of microfluidic systems with different microchannel geometries (microchannel with teardrop micromixers and microchannel with swirl micromixers) for the preparation of oil-in-water (O/W) emulsions using two different emulsifiers (2 % and 4 % Tween 20 and 2% and 4 % PEG 2000) at total flow rates of 20-280 μL/min was investigated. The results showed that droplets with a smaller average Feret diameter were obtained when a microfluidic device with tear drop micromixers was used. To predict the average Feret diameter of O/W emulsion droplets, near-infrared (NIR) spectra of all prepared emulsions were collected and coupled with partial least squares (PLS) regression and artificial neural network modelling (ANN). The results showed that PLS models based on NIR spectra can ensure acceptable qualitative prediction, while highly non-linear ANN models are more suitable for predicting the average Feret diameter of O/W droplets. High R² values (R²_validation greater than 0.8) confirm that ANNs can be used to monitor the emulsification process.

Multi-Way Analysis Coupled with Near-Infrared Spectroscopy in Food Industry: Models and Applications

Near-infrared spectroscopy (NIRS) is a fast and powerful analytical tool in the food industry. As an advanced chemometrics tool, multi-way analysis shows great potential for solving a wide range of food problems and analyzing complex spectroscopic data. This paper describes the representative multi-way models which were used for analyzing NIRS data, as well as the advances, advantages and limitations of different multi-way models. The applications of multi-way analysis in NIRS for the food industry in terms of food process control, quality evaluation and fraud, identification and classification, prediction and quantification, and image analysis are also reviewed. It is evident from this report that multi-way analysis is presently an attractive tool for modeling complex NIRS data in the food industry while its full potential is far from reached. The combination of multi-way analysis with NIRS will be a promising practice for turning food data information into operational knowledge, conducting reliable food analyses and improving our understanding about food systems and food processes. To the best of our knowledge, this is the first paper that systematically reports the advances on models and applications of multi-way analysis in NIRS for the food industry.