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Keywords = Porphyra residues

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16 pages, 2175 KB  
Article
Comparative Analysis of XGB, CNN, and ResNet Models for Predicting Moisture Content in Porphyra yezoensis Using Near-Infrared Spectroscopy
by Wenwen Zhang, Mingxuan Pan, Peng Wang, Jiao Xue, Xinghu Zhou, Wenke Sun, Yadong Hu and Zhaopeng Shen
Foods 2024, 13(19), 3023; https://doi.org/10.3390/foods13193023 - 24 Sep 2024
Cited by 6 | Viewed by 1875
Abstract
This study explored the performance and reliability of three predictive models—extreme gradient boosting (XGB), convolutional neural network (CNN), and residual neural network (ResNet)—for determining the moisture content in Porphyra yezoensis using near-infrared (NIR) spectroscopy. We meticulously selected 380 samples from various sources to [...] Read more.
This study explored the performance and reliability of three predictive models—extreme gradient boosting (XGB), convolutional neural network (CNN), and residual neural network (ResNet)—for determining the moisture content in Porphyra yezoensis using near-infrared (NIR) spectroscopy. We meticulously selected 380 samples from various sources to ensure a comprehensive dataset, which was then divided into training (300 samples) and test sets (80 samples). The models were evaluated based on prediction accuracy and stability, employing genetic algorithms (GA) and partial least squares (PLS) for wavelength selection to enhance the interpretability of feature extraction outcomes. The results demonstrated that the XGB model excelled with a determination coefficient (R2) of 0.979, a root mean square error of prediction (RMSEP) of 0.004, and a high ratio of performance to deviation (RPD) of 4.849, outperforming both CNN and ResNet models. A Gaussian process regression (GPR) was employed for uncertainty assessment, reinforcing the reliability of our models. Considering the XGB model’s high accuracy and stability, its implementation in industrial settings for quality assurance is recommended, particularly in the food industry where rapid and non-destructive moisture content analysis is essential. This approach facilitates a more efficient process for determining moisture content, thereby enhancing product quality and safety. Full article
(This article belongs to the Special Issue AI-Powered Advances in Data Handling for Enhanced Food Analysis: From Chemometrics to Machine Learning)
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11 pages, 3698 KB  
Article
Production of Phenyllactic Acid from Porphyra Residues by Lactic Acid Bacterial Fermentation
by Chung-Hsiung Huang, Wei-Chen Chen, Yu-Huei Gao, Hsin-I Hsiao and Chorng-Liang Pan
Processes 2021, 9(4), 678; https://doi.org/10.3390/pr9040678 - 13 Apr 2021
Cited by 13 | Viewed by 3169
Abstract
The concept of algae biorefinery is attracting attention because of the abundant valuable compounds within algal biomass. Phenyllactic acid (PhLA), a broad-spectrum antimicrobial organic acid that can be produced by lactic acid bacteria (LAB), is considered a potential biopreservative. In this study, a [...] Read more.
The concept of algae biorefinery is attracting attention because of the abundant valuable compounds within algal biomass. Phenyllactic acid (PhLA), a broad-spectrum antimicrobial organic acid that can be produced by lactic acid bacteria (LAB), is considered a potential biopreservative. In this study, a cascading biorefinery approach was developed to harvest PhLA from Porphyra residues by LAB fermentation. LAB strains were cultivated in de Man, Rogosa and Sharpe (MRS) broth to screen their ability to produce PhLA. As the strains of Lactobacillus plantarum KP3 and L. plantarum KP4 produced higher concentrations of PhLA at 0.09 mg/mL, these two strains were employed for fermentation. After phycobiliprotein extraction, the Porphyra residues, ultrafiltration eluate, phenylalanine (Phe) and yeast extract with a volume of 20 mL were inoculated with LAB strain KP3 and fermented at 37 °C for 120 h. The PhLA content of the fermented broth was 1.86 mg. To optimize the biorefinery process, the ultrafiltration eluate was replaced by commercial cellulase. Up to 4.58 mg of PhLA, which was 2.5 times greater than that produced from KP3 cultured in MRS broth, could be harvested. Taken together, the findings provide an optimized process for LAB fermentation, which is shown to be a feasible algae biorefinery approach to obtaining PhLA from Porphyra residues. Full article
(This article belongs to the Special Issue Application of Proteomics and Enzyme Technologies in Foods)
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