光谱学与光谱分析 |
|
|
|
|
|
Kinetic Models for Determination of Yeast in Fresh Jujube Using Near Infrared Spectroscopy |
HU Yao-hua1, LIU Cong1, HE Yong2* |
1. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China 2. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
|
|
Abstract The objectives of this study were: (1) to optimize a near-infrared (NIR) spectroscopy model for fresh jujube stored at room temperature to predict the quality change (yeast growth), (2) to establish a kinetic model of yeast growth for fresh jujubes at room temperature according to NIR spectroscopy data and storage time, and (3) to predict the shelf life of fresh jujube at room temperature. The Lizao samples of fresh jujubes were adopted as the research object in the study. The NIR spectral data were achieved before yeast infection level measured. In order to optimize the NIR model, the pretreatment techniques such as Savitzky-Golay smoothing (S-G smoothing), multiplicative scatter correction (MSC), first derivative (1-Der) and second derivative (2-Der) were compared with the raw spectra by using a statistical software package (Unscrambler 9.8), and the regression coefficient (RC) method was used to choose the characteristic wavenumber. Multiple linear regression (MLR) was applied as NIR modeling method. According to the predicted yeast infection level using NIR model, the chemical kinetic models of spectral data and storage time at room temperature with zero-order and first-order reaction were established by using a statistical software package (SPSS 18). The shelf life could be predicted based on the chemical kinetic model. The results showed that the characteristic wave numbers of 10 300, 8 330, 6 900, 5 666, 5 150 and 4 060 cm-1 in the whole near-infrared range with MSC technique could be chosen to model the quality deterioration of fresh jujube at room temperature. The NIR model that produced the best prediction had the form of B=320.027-233.920x1-206.663x2-61.584x3-14.847x4-2.680x5-9.131x6, where B is yeast value (lg/cfu·g-1), x1~x6 are absorbance value of characteristic wavenumber. The correlation coefficient of calibration (Rc) was 0.950, the root mean square error of calibration (RMSEC) was 2.560, the correlation coefficient of prediction (Rp) was 0.863, and the root mean square error of prediction (RMSEP) was 2.447.The zero-order reaction kinetic model performed better than the first-order model. The zero-order reaction kinetic model of yeast growth with storage time was predicted by Bt=171.395-124.445x1-109.945x2-32.763x3-7.899x4-1.426x5-4.857x6+0.045t with a correlation coefficient of 0.996. Based on the linear correlation between the NIR measurement and storage time, the shelf life of fresh jujube at room temperature was predicted to be 8 days for the yeast infection level less than 10 cfu·g-1. The study showed that the NIR when combed with kinetic models could be used as a non-destructive, rapid method to detect the yeast growth in fresh jujube, and to predict the shelf life and ensure the quality and safety of fresh jujube.
|
Received: 2013-05-25
Accepted: 2013-08-10
|
|
Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
|
|
[1] LIU Meng-jun(刘孟军). Jujube High Quality Production Technology Handbook (枣优质生产技术手册). Beijing:China Agriculture Press (北京:中国农业出版社), 2004. 227. [2] REN Ke, TU Kang, PAN Lei-qing, et al(任 珂, 屠 康, 潘磊庆, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2005, 21(8): 146. [3] GAO Yuan-jun, HAO Li-hua, ZHANG Xin, et al(高愿军, 郝莉花, 张 鑫, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2006, 22(5): 157. [4] TONG Yi,XIE Jing(佟 懿,谢 晶). Food Science(食品科学), 2009, 30(10): 265. [5] LIN Jin, YANG Rui-jin, ZHANG Wen-bin, et al(林 进, 杨瑞金, 张文斌, 等). Food Science(食品科学), 2009, 30(22): 361. [6] ZHAO Si-ming, LI Hong-xia, XIONG Shan-bai, et al(赵思明, 李红霞, 熊善柏, 等). Food Science(食品科学), 2002, 23(8):80. [7] Cao Fang, Wu Di, He Yong. Computers and Electronics in Agriculture, 2010, 71: 15. [8] Burks C S, Dowell F E, Xie F. Journal of Stored Products Research, 2000, 36: 289. [9] Clark C J, McGlone V A, Silva H N D, et al. Postharvest Biology and Technology, 2003, 32: 147. [10] Clark C J, McGlone V A, Jordan R B. Postharvest Biology and Technology, 2003, 28: 87. [11] Fu Xia-ping, Ying Yi-bin, Lu Hui-shan, et al. Journal of Food Engineering, 2007, 83: 317. [12] Wang J, Nakano K, Ohashi S. Postharvest Biology and Technology, 2010, 59: 272. [13] LIU Cong, GUO Kang-quan, ZHANG Qiang, et al(刘 聪, 郭康权, 张 强, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(1): 278. |
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[13] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|