Research and Application of Soft Sensor Modeling Method for Total Sugar Content in the Fermentation Process of Chlortetracycline

AbstractIn order to solve the problem that the total sugar content of the chlortetracycline fermentation tank can not be automatically detected online, a prediction method which combines the output recursive wavelet neural network and the Gauss process regression is proposed in this paper. A soft sensor model between the measurable parameters (inputs) and the total sugar content (output) of the chlortetracycline fermentation tank was established. The soft sensor model was trained by self updating algorithm. Based on field data, the accuracy and generalization ability of the soft sensor model were analyzed. It is shown that the prediction accuracy of the combined model proposed in this paper is better than that of other single models. The results demonstrate the superiority of the method, and MRE and RMSE are used to evaluate the performance of the soft sensor model. It shows that the prediction precision of the soft sensor model based on ORWNN-GPR combination is relatively high in the long period of fermentation, and is suitable for on-line prediction of the total sugar content of the chlortetracycline fermentation tank. The soft sensor method can effectively reduce the labor intensity of the analysts and saves the production cost for enterprise.

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Organic brown sugar and jaboticaba pulp influence on water kefir fermentation

Ciência e Agrotecnologia ◽

10.1590/1413-7054201943005619 ◽

2019 ◽

Vol 43 ◽

Cited By ~ 2

Author(s):

Tainá Miranda Destro ◽

Denise da Fontoura Prates ◽

Lycio Shinji Watanabe ◽

Sandra Garcia ◽

Guilherme Biz ◽

...

Keyword(s):

Functional Food ◽

Fermentation Process ◽

Sugar Content ◽

Total Sugar ◽

Brown Sugar ◽

Total Sugar Content ◽

Color Profile ◽

Kefir Grains ◽

Two Stages ◽

Water Kefir

ABSTRACT Water kefir is considered to be a functional food with probiotic characteristics. It can be improved with addition of organic brown sugar and fruits native to Brazil, such as jaboticaba. The objective of this study was to evaluate the effects of brown sugar type: organic and conventional and the presence or absence of jaboticaba pulp in water kefir fermentation, based on the carbohydrate, organic acid, mineral composition and color profile. The fermentation process was carried out in two stages: with kefir grains, for 24 hours, at 25 °C and after filtration, maintained for 24 h at 25 °C in hermetic bottles, followed by a period of 8h refrigeration, and monitored every 8 h. Scanning electron microscopy of the kefir grains was performed after 24 h. The major changes in physicochemical patterns occurred up to 40 h. The final beverages presented acidic characteristics as a result of the production of lactic, acetic and succinic acids. The total sugar content had a reduction of around 50%. The sucrose content was reduced and the glucose and fructose increased. The use of organic sugar influenced the composition of the minerals. The characteristics related to color showed a tendency to increase over the evaluated times. It was concluded that it is possible to produce beverages fermented by water kefir grains, using organic or conventional brown sugar and jabuticaba pulp, being an alternative for the substitution of soft drinks, since it has no preservatives or food colorings, presents lower content of sugars and can be handcrafted.

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Study on soft sensor modeling method for sign of contaminated fermentation broth in Chlortetracycline fermentation process

Preparative Biochemistry & Biotechnology ◽

10.1080/10826068.2020.1793173 ◽

2020 ◽

Vol 51 (1) ◽

pp. 76-85 ◽

Cited By ~ 1

Author(s):

Mei-chun Wang ◽

Xiang Han ◽

Yu-mei Sun ◽

Qiao-yan Sun ◽

Xiang-guang Chen

Keyword(s):

Fermentation Process ◽

Fermentation Broth ◽

Soft Sensor ◽

Modeling Method ◽

Sensor Modeling

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Study on the prediction of the contamination symptoms in the fermentation process of Chlortetracycline based on soft sensor modeling method

Technology and Health Care ◽

10.3233/thc-199020 ◽

2019 ◽

Vol 27 ◽

pp. 205-215

Author(s):

Yumei Sun ◽

Lingtong Tang ◽

Qiaoyan Sun ◽

Meichun Wang ◽

Xiang Han ◽

...

Keyword(s):

Fermentation Process ◽

Soft Sensor ◽

Modeling Method ◽

Sensor Modeling

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Study on Multi-Model Soft Sensor Modeling Method and Its Model Optimization for the Fermentation Process of Pichia pastoris

Sensors ◽

10.3390/s21227635 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7635

Author(s):

Bo Wang ◽

Xingyu Wang ◽

Mengyi He ◽

Xianglin Zhu

Keyword(s):

Pichia Pastoris ◽

Least Squares ◽

Fermentation Process ◽

Model Simulation ◽

Weighted Least Squares ◽

Soft Sensor ◽

Modeling Method ◽

Local Model ◽

Support Vector ◽

Sensor Modeling

The problems that the key biomass variables in Pichia pastoris fermentation process are difficult measure in real time; this paper mainly proposes a multi-model soft sensor modeling method based on the piecewise affine (PWA) modeling method, which is optimized by particle swarm optimization (PSO) with an improved compression factor (ICF). Firstly, the false nearest neighbor method was used to determine the order of the PWA model. Secondly, the ICF-PSO algorithm was proposed to cooperatively optimize the number of PWA models and the parameters of each local model. Finally, a least squares support vector machine was adopted to determine the scope of action of each local model. Simulation results show that the proposed ICF-PSO-PWA multi-model soft sensor modeling method accurately approximated the nonlinear features of Pichia pastoris fermentation, and the model prediction accuracy is improved by 4.4884% compared with the weighted least squares vector regression model optimized by PSO.

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Physico-chemical and microbiological changes during the cabbage hybrid bravo heads fermentation: Salt, temperature and starter culture influence

Food Science and Technology International ◽

10.1177/10820132211036308 ◽

2021 ◽

pp. 108201322110363

Author(s):

Mirna Drašković Berger ◽

Anita Vakula ◽

Aleksandra Tepić Horecki ◽

Tatjana Peulić ◽

Marija Jokanović ◽

...

Keyword(s):

Principal Component Analysis ◽

Fermentation Process ◽

Sugar Content ◽

Starter Culture ◽

Salt Content ◽

Principal Component ◽

Total Sugar ◽

Total Acidity ◽

Total Sugar Content ◽

Physico Chemical

The influence of temperatures (18, 22 and 26°C), salt (60, 70 and 80 g/kg) and starter culture (0, 0.025 and 0.050 g/kg) on physico-chemical and microbiological changes during fermentation in cabbage heads were investigated. The experiment was set up in industrial conditions, samples were taken after 0, 5, 12, 27 and 62 days in order to determine the changes in color, texture, biogenic amines content, microbiological parameters, pH, aw, total sugar content, total acidity and salt content. Analysis of variance and Principal component analysis were applied to get a better overview of differences between samples and the similarities between different fermentation conditions. Significant color changes and softening of cabbage tissue did not occurred during the entire fermentation process. Cadaverine was not detected in samples containing starter culture, also the results were in range for maximum allowed putrescine and cadaverine doses for all cabbage samples. Total number of molds increased, contrary to number of yeasts on the last examined day in comparison with raw cabbage sample. Enterobacteriaceae were not detected after 5th fermentation day, while sharp increase of lactic acid bacteria number occurred until 12th day, and on the 12th fermentation day was in range of 7.82 to 9.84 log cfu/g. The fermentation process led to decrease in pH, aw and total sugar content, meanwhile total acidity 0.54%-0.89% and salt content 2.08%-3.19% in the cabbage heads increased, in comparison with raw sample where results for total acidity and salt content were 0.13% and 0.05%, respectively. Additionally, according Principal component analysis, temperature of fermentation had the greatest influence on the fermentation process, followed by the application of starter culture and then concentration of added salt.

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Mid and near Infrared Study of Carbohydrates by Canonical Correlation Analysis

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.13 ◽

1993 ◽

Vol 1 (2) ◽

pp. 99-108 ◽

Cited By ~ 13

Author(s):

P. Robert ◽

M.F. Devaux ◽

A. Qannari ◽

M. Safar

Keyword(s):

Correlation Analysis ◽

Infrared Spectra ◽

Canonical Correlation ◽

Near Infrared ◽

Sugar Content ◽

Infrared Region ◽

Total Sugar ◽

Mid Infrared ◽

Total Sugar Content ◽

Near Infrared Spectra

Multivariate data treatments were applied to mid and near infrared spectra of glucose, fructose and sucrose solutions in order to specify near infrared frequencies that characterise each carbohydrate. As a first step, the mid and near infrared regions were separately studied by performing Principal Component Analyses. While glucose, fructose and sucrose could be clearly identified on the similarity maps derived from the mid infrared spectra, only the total sugar content of the solutions was observed when using the near infrared region. Characteristic wavelengths of the total sugar content were found at 2118, 2270 and 2324 nm. In a second step, the mid and near infrared regions were jointly studied by a Canonical Correlation Analysis. As the assignments of frequencies are generally well known in the mid infrared region, it should be useful to study the relationships between the two infrared regions. Thus, the canonical patterns obtained from the near infrared spectra revealed wavelengths that characterised each carbohydrate. The OH and CH combination bands were observed at: 2088 and 2332 nm for glucose, 2134 and 2252 nm for fructose, 2058 and 2278 nm for sucrose. Although a precise assignment of the near infrared bands to chemical groups within the molecules was not possible, the present work showed that near infrared spectra of carbohydrates presented specific features.

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