Changzhou Zuo
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Changzhou Zuo has a total of 17 co-authors. Here are the top co-authors by number of shared publications:
- Kang Tu
College of Food Science and Technology, Nanjing Agricultural University, Nanjing, 210095, China
4 shared publications - Leiqing Pan
College of Food Science and Technology Nanjing Agricultural University Nanjing Jiangsu China
4 shared publications - Weijie Lan
College of Food Science and Technology Nanjing Agricultural University Nanjing Jiangsu China
4 shared publications - Zhenjie Wang
College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
3 shared publications - Jingyuan Zhao
College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China
2 shared publications - Mengyao Wang
College of Food Science and Technology, Nanjing Agricultural University, No. 1 Weigang Road, Nanjing 210095, China; Sanya Institute of Nanjing Agricultural University, Sanya, Hainan, 572024, China
2 shared publications
Latest Publications
- Top Cited Papers
- 19 February 2025
- Published by Elsevier in Food Chemistry
- Vol. 477, 143504
Exploring the optical response of water status and light propagation in bruised ‘Korla’ fragrant pear tissues based on low-field nuclear magnetic resonance coupled with Monte Carlo simulation
- 19 February 2025
- Published by Elsevier in Food Chemistry
- Vol. 477, 143504
Top Cited PapersExploring the optical response of water status and light propagation in bruised ‘Korla’ fragrant pear tissues based on low-field nuclear magnetic resonance coupled with Monte Carlo simulation
- Open Access
Storage Properties and Shelf-Life Prediction of Fresh-Cut Radishes Treated by Photodynamic Technology
Fresh-cut radishes are susceptible to quality loss and microbial contamination during storage, resulting in a short shelf life. This study investigated the effects of photodynamic technology (PDT) on fresh-cut radishes stored at 4 °C for 10 d and developed appropriate models to predict the shelf life. Results showed that curcumin-mediated PDT maintained sensory acceptability, color, and firmness, decreased weight loss, and increased ascorbic acid and total phenolics of samples by inactivating polyphenol oxidase and peroxidase, resulting in improved antioxidant capacity and quality. The total bacteria count in samples was significantly (p < 0.05) reduced by 2.01 log CFU g−1 after PDT and their shelf life was extended by 6 d compared to the control. To accurately predict the shelf life, the kinetic models based on microbial growth were established, while weight loss, b* value, firmness, and ascorbic acid were selected as representative attributes for developing quality-based prediction models through correlation analysis. Modeling results showed prediction models based on ascorbic acid best fitted PDT-treated samples, while the modified Gompertz model based on bacteria growth was the best for control and samples treated by sodium hypochlorite. This study suggests that PDT is promising in extending the shelf life of fresh-cut radishes, and using critical indexes to establish the prediction model can provide a more reliable shelf-life estimation.
Open AccessStorage Properties and Shelf-Life Prediction of Fresh-Cut Radishes Treated by Photodynamic Technology
Fresh-cut radishes are susceptible to quality loss and microbial contamination during storage, resulting in a short shelf life. This study investigated the effects of photodynamic technology (PDT) on fresh-cut radishes stored at 4 °C for 10 d and developed appropriate models to predict the shelf life. Results showed that curcumin-mediated PDT maintained sensory acceptability, color, and firmness, decreased weight loss, and increased ascorbic acid and total phenolics of samples by inactivating polyphenol oxidase and peroxidase, resulting in improved antioxidant capacity and quality. The total bacteria count in samples was significantly (p < 0.05) reduced by 2.01 log CFU g−1 after PDT and their shelf life was extended by 6 d compared to the control. To accurately predict the shelf life, the kinetic models based on microbial growth were established, while weight loss, b* value, firmness, and ascorbic acid were selected as representative attributes for developing quality-based prediction models through correlation analysis. Modeling results showed prediction models based on ascorbic acid best fitted PDT-treated samples, while the modified Gompertz model based on bacteria growth was the best for control and samples treated by sodium hypochlorite. This study suggests that PDT is promising in extending the shelf life of fresh-cut radishes, and using critical indexes to establish the prediction model can provide a more reliable shelf-life estimation.
- 18 July 2024
- Published by Elsevier in Food Research International
- Vol. 192, 114787
Assessment of optical properties and Monte-Carlo based simulation of light propagation in citrus infected by Penicillium italicum
- 18 July 2024
- Published by Elsevier in Food Research International
- Vol. 192, 114787
Assessment of optical properties and Monte-Carlo based simulation of light propagation in citrus infected by Penicillium italicum
- Open Access
A Novel Variable Selection Method Based on Ordered Predictors Selection and Successive Projections Algorithm for Predicting Gastrodin Content in Fresh Gastrodia elata Using Fourier Transform Near-Infrared Spectroscopy and Chemometrics
Gastrodin is one of the most important biologically active components of Gastrodia elata, which has many health benefits as a dietary and health food supplement. However, gastrodin measurement traditionally relies on laboratory and sophisticated instruments. This research was aimed at developing a rapid and non-destructive method based on Fourier transform near infrared (FT-NIR) to predict gastrodin content in fresh Gastrodia elata. Auto-ordered predictors selection (autoOPS) and successive projections algorithm (SPA) were applied to select the most informative variables related to gastrodin content. Based on that, partial least squares regression (PLSR) and multiple linear regression (MLR) models were compared. The autoOPS-SPA-MLR model showed the best prediction performances, with the determination coefficient of prediction (), ratio performance deviation (RPD) and range error ratio (RER) values of 0.9712, 5.83 and 27.65, respectively. Consequently, these results indicated that FT-NIRS technique combined with chemometrics could be an efficient tool to rapidly quantify gastrodin in Gastrodia elata and thus facilitate quality control of Gastrodia elata.Open AccessA Novel Variable Selection Method Based on Ordered Predictors Selection and Successive Projections Algorithm for Predicting Gastrodin Content in Fresh Gastrodia elata Using Fourier Transform Near-Infrared Spectroscopy and Chemometrics
Gastrodin is one of the most important biologically active components of Gastrodia elata, which has many health benefits as a dietary and health food supplement. However, gastrodin measurement traditionally relies on laboratory and sophisticated instruments. This research was aimed at developing a rapid and non-destructive method based on Fourier transform near infrared (FT-NIR) to predict gastrodin content in fresh Gastrodia elata. Auto-ordered predictors selection (autoOPS) and successive projections algorithm (SPA) were applied to select the most informative variables related to gastrodin content. Based on that, partial least squares regression (PLSR) and multiple linear regression (MLR) models were compared. The autoOPS-SPA-MLR model showed the best prediction performances, with the determination coefficient of prediction (), ratio performance deviation (RPD) and range error ratio (RER) values of 0.9712, 5.83 and 27.65, respectively. Consequently, these results indicated that FT-NIRS technique combined with chemometrics could be an efficient tool to rapidly quantify gastrodin in Gastrodia elata and thus facilitate quality control of Gastrodia elata.