Toward enhancement in prediction of Pseudomonas counts distribution in salmon fillets using NIR hyperspectral imaging

Assessment of Spoilage Microbiota of Rainbow Trout (Oncorhynchus mykiss) during Storage by 16S rDNA Sequencing

Due to the high contents of protein and fat in rainbow trout, it is highly susceptible to spoilage, which limits the storage and transportation processes. Exploring the spoilage microbial community during rainbow trout storage is essential to develop an effective preservation method. Here, the changes in the total bacterial colony and total volatile base nitrogen (TVB-N) during the storage of rainbow trout were investigated. Storage at 0 °C can effectively slow down the spoilage process with bacterial counts and TVB-N contents decreased from 8.7 log CFU/g and 18.7 mg/100 g obtained at 4 °C to 5.6 log CFU/g and 14.5 mg/100 g, respectively. 16S rDNA high-throughput sequencing results showed that the diversity of microbial genera decreased during storage. Acinetobacter, Pseudomonas, and Shewanells gradually became the dominant spoilage genera with contents of 59.9%, 18.6%, and 1.7%, respectively, in the late stage of storage. The spoilage abilities of bacteria belonging to the Pseudomonas and Shewanells genera were analyzed. Shewanella sp. S5-52 showed the highest level of TVB-N content (100.6 mg/100 g) in sterile fish juice, indicating that it had a strong spoilage ability. This study confirmed the dominant spoilage bacterial genera and evaluated the spoilage abilities of isolated strains during the storage of rainbow trout, which laid the foundation for further investigation of the spoilage mechanism of rainbow trout and other aquatic products.

Spectroscopy and imaging technologies coupled with machine learning for the assessment of the microbiological spoilage associated to ready-to-eat leafy vegetables

Based on both new and previously utilized experimental data, the present study provides a comparative assessment of sensors and machine learning approaches for evaluating the microbiological spoilage of ready-to-eat leafy vegetables (baby spinach and rocket). Fourier-transform infrared (FTIR), near-infrared (NIR), visible (VIS) spectroscopy and multispectral imaging (MSI) were used. Two data partitioning approaches and two algorithms, namely partial least squares regression and support vector regression (SVR), were evaluated. Concerning baby spinach, when model testing was performed on samples randomly selected, the performance was better than or similar to the one attained when testing was performed based on dynamic temperatures data, depending on the applied analytical technology. The two applied algorithms yielded similar model performances for the majority of baby spinach cases. Regarding rocket, the random data partitioning approach performed considerably better results in almost all cases of sensor/algorithm combination. Furthermore, SVR algorithm resulted in considerably or slightly better model performances for the FTIR, VIS and NIR sensors, depending on the data partitioning approach. However, PLSR algorithm provided better models for the MSI sensor. Overall, the microbiological spoilage of baby spinach was better assessed by models derived mainly from the VIS sensor, while FTIR and MSI were more suitable in rocket. According to the findings of this study, a distinct sensor and computational analysis application is needed for each vegetable type, suggesting that there is not a single combination of analytical approach/algorithm that could be applied successfully in all food products and throughout the food supply chain.

Identification of coffee bean varieties using hyperspectral imaging: influence of preprocessing methods and pixel-wise spectra analysis

Hyperspectral imaging was used to identify and to visualize the coffee bean varieties. Spectral preprocessing of pixel-wise spectra was conducted by different methods, including moving average smoothing (MA), wavelet transform (WT) and empirical mode decomposition (EMD). Meanwhile, spatial preprocessing of the gray-scale image at each wavelength was conducted by median filter (MF). Support vector machine (SVM) models using full sample average spectra and pixel-wise spectra, and the selected optimal wavelengths by second derivative spectra all achieved classification accuracy over 80%. Primarily, the SVM models using pixel-wise spectra were used to predict the sample average spectra, and these models obtained over 80% of the classification accuracy. Secondly, the SVM models using sample average spectra were used to predict pixel-wise spectra, but achieved with lower than 50% of classification accuracy. The results indicated that WT and EMD were suitable for pixel-wise spectra preprocessing. The use of pixel-wise spectra could extend the calibration set, and resulted in the good prediction results for pixel-wise spectra and sample average spectra. The overall results indicated the effectiveness of using spectral preprocessing and the adoption of pixel-wise spectra. The results provided an alternative way of data processing for applications of hyperspectral imaging in food industry.

Study on the spoilage potential of Pseudomonas fluorescens on salmon stored at different temperatures

The bacterial kinetics and quality indexes [sensory quality, total volatile basic nitrogen (TVB-N), thiobarbituric acid value, biogenic amine, and amino acids] were analyzed on salmon inoculated with Pseudomonas fluorescens during storage under different temperatures (30, 10, and 4 °C). The bacterial kinetics revealed that P. fluorescens showed a steady growth at low temperatures (10 and 4 °C). The TVB-N yield factors of the sample stored at 4 °C indicated that each bacterial cell of P. fluorescens displayed greater spoilage activity at low temperatures. A remarkable correlation was found between the production of biogenic amines and bacterial counts. The results also highlighted that P. fluorescens cultured at 4 °C had higher demand for amino acids.

A feasibility study of non-targeted adulterant screening based on NIRM spectral library of soybean meal to guarantee quality: The example of non-protein nitrogen

The quality and safety of soybean meal is a key matter for the livestock breeding and food industries, since it is one of the most important and widely used protein feed raw materials. As driven by commercial interests, new illegal adulterants which are unknown to consumers and regulators emerge constantly. In order to make up for the inadequacy of traditional detection methods, a novel non-targeted adulterant screening method based on a near-infrared microscopy spectral library of soybean meal is proposed. This study focused on the feasibility of non-targeted screening methods for the detection of adulteration in soybean meal. Six types of non-protein nitrogen were taken as examples and partial least squares discriminant analysis was employed to verify the feasibility of this novel method. The results showed that the non-targeted screening method could screen out adulterations in soybean meal with satisfactory results.