Packaged food detection method based on the generalized Gaussian model for line-scan Raman scattering images

Multifunctional Sensors Made with Conductive Microframework and Biomass Hydrogel for Detecting Packaging Pressure and Food Freshness

Food packaging detection devices have attracted attention to optimize storage situations and reduce food spoilage. However, low-cost and highly sensitive multifunctional sensors for detecting both food freshness and packaging pressure are still lacking. In this study, a multifunctional sensor was developed consisting of a MXene coated alcohol-soluble polyurethane fiber network (MXene/APU) and composite biohydrogel films made of konjac glucomannan, chitosan, and blueberry anthocyanin (KCB). Based on the pressure sensitivity of MXene/APU and the color changes of KCB in response to pH values, the sensor can detect internal package bulging, external squeezing, and food deterioration. The pressure sensor shows a sensitivity of 1.16 kPa-1, a response time of 200 ms, a wide strain range of 1092%, and stability over multiple loops. The pressure sensor could detect human motion and identify surface morphologies. The excellent sensor performance was attributed to the porous structure and large specific surface area of microfiber networks, conductivity of MXene nanosheets, and protective effect of KCB films coated on the conductive membrane. Besides, the microfluidic blow-spinning method used to prepare microfiber networks showed the advantages of low energy consumption and high production efficiency. Based on the color changes of blueberry anthocyanin loaded in KCB films in response to pH, the sensor realized sensitive spoilage detection of food containing protein. This study provides a new multifunctional food packaging sensing device and a greater understanding of the optimization and application of related devices.

Evaluating performance of SORS-based subsurface signal separation methods using statistical replication Monte Carlo simulation

Spatially offset Raman spectroscopy (SORS) is a depth-profiling technique with deep information enhancement. However, the interference of the surface layer cannot be eliminated without prior information. The signal separation method is an effective candidate for reconstructing pure subsurface Raman spectra, and there is still a lack of evaluation means for the signal separation method. Therefore, a method based on line-scan SORS combined with improved statistical replication Monte Carlo (SRMC) simulation was proposed to evaluate the effectiveness of food subsurface signal separation method. Firstly, SRMC simulates the photon flux in the sample, generates a corresponding number of Raman photons at each voxel of interest, and collects them by external map scanning. Then, 5625 groups of mixed signals with different optical characteristic parameters were convoluted with spectra of public database and application measurement and introduced into signal separation methods. The effectiveness and application range of the method were evaluated by the similarity between the separated signals and the source Raman spectra. Finally, the simulation results were verified by three packaged foods. FastICA method can effectively separate Raman signals from subsurface layer of food and thus promote deep quality evaluation of food.

Spatially Offset Raman Spectroscopy Combined with Attention-Based LSTM for Freshness Evaluation of Shrimp

Optical detection of the freshness of intact in-shell shrimps is a well-known difficult task due to shell occlusion and its signal interference. The spatially offset Raman spectroscopy (SORS) is a workable technical solution for identifying and extracting subsurface shrimp meat information by collecting Raman scattering images at different distances from the offset laser incidence point. However, the SORS technology still suffers from physical information loss, difficulties in determining the optimum offset distance, and human operational errors. Thus, this paper presents a shrimp freshness detection method using spatially offset Raman spectroscopy combined with a targeted attention-based long short-term memory network (attention-based LSTM). The proposed attention-based LSTM model uses the LSTM module to extract physical and chemical composition information of tissue, weight the output of each module by an attention mechanism, and come together as a fully connected (FC) module for feature fusion and storage dates prediction. Modeling predictions by collecting Raman scattering images of 100 shrimps within 7 days. The R², RMSE, and RPD of the attention-based LSTM model achieved 0.93, 0.48, and 4.06, respectively, which is superior to the conventional machine learning algorithm with manual selection of the optimal spatially offset distance. This method of automatically extracting information from SORS data by Attention-based LSTM eliminates human error and enables fast and non-destructive quality inspection of in-shell shrimp.

A packaged food internal Raman signal separation method based on spatially offset Raman spectroscopy combined with FastICA

Raman spectroscopy attempts to reflect food quality by characterizing molecular vibration and rotation. However, the blocking of optical signals by packaging materials and the interference of the optical signal generated by the packaging itself make the detection of internal food quality without destroying packaging highly difficult. In this regard, this paper proposes a novel packaged food internal signal separation based on spatially offset Raman spectroscopy (SORS) coupled with improved fast independent component analysis (FastICA). Firstly, the Raman scattering image of the packaged food with offset laser incident point was obtained. Then, the movable quadratic mean of information entropy was used to select the observation feature region of the image. Thirdly, the main independents decomposed by the optimized FastICA method were identified by spectral attenuation characteristics of the SORS peak signal. Finally, the non-negativity of the separated signal was ensured by baseline recognition and correction. The effectiveness of this method was verified by refactoring the similarity between the signal and the reference signal by testing three different packaging and four internal materials under standard experimental conditions. The applicability of the method was proved by the internal signal separation of three packaged foods on sale. The experimental results indicate that the proposed method can separate the Raman signal of packaged food and can be used as a pretreatment method and auxiliary analysis means for the detection of packaged food.

Detection of adulterated sugar with plastic packaging based on spatially offset Raman imaging

BACKGROUND

The application of optical sensing technology in food adulteration detection has been extensively studied. However, due to the impact of packaging materials on the penetration depth of photons in foods and the interference from the optical properties of the packaging materials themselves, the use of optical sensing technology to detect packaged foods adulteration is still a well-known problem.

RESULTS

The line-scan Raman imaging system was used to collect Raman hyperspectral images of adulterated sugars, made by mixing soft sugar and cheap glucose in seven different ratios. With the 0 and 3 mm (optimal offset distance) between line-laser source and scanning line, the Raman hyperspectral images of adulterated sugars covered by packaging plastic were acquired respectively. Using adulterated samples un-covered by packaging plastic as training samples, the Random Forest prediction model was developed, and excellent prediction performance was achieved for adulterated samples un-covered by packaging plastics. Compared with Raman data acquired with 0 mm offset distance, the performance of the prediction model was significantly improved, with 0.957 for coefficient of determination (R² ), 0.413 for root mean square error of prediction (RMSEP), and 4.846 for residual predictive deviation (RPD), for adulterated samples with plastic packaging acquired with the 3 mm offset distance.

CONCLUSIONS

The novel non-destructive method based on spatially offset Raman imaging technology, which can reduce the interference of packaging materials and enhance the signal of internal interesting materials, was proposed for detection of adulterated sugar with plastic packaging. The experiment results show that spatially offset imaging technology provides a candidate method for detecting adulteration of packaged foods. © 2021 Society of Chemical Industry.

Line-scan Raman scattering image and multivariate analysis for rapid and noninvasive detection of restructured beef

The mean spectral (MS) features were extracted from Raman scattering images (RSI) of beef samples over the region of interest covering the spectral range of 789-1710cm^-1 and the spatial offset range of 0-5 mm (for two sides of the incident laser). The RSI monitored the main change in the protein, amide bands, lipids, and amino acid residues. The classification model performance based on MS features compared the conventional Raman spectral features and confirmed the usefulness of RSI. Finally, the results showed that RSI technology is a reliable tool for rapid and noninvasive detection of restructured beef.