Growth Prediction of Alicyclobacillus acidoterrestris in Orange Juice Based on Near-infrared Spectroscopy
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摘要: 酸土脂环酸芽孢杆菌（Alicyclobacillus acidoterrestris）是引起橙汁劣变的主要微生物，为研究酸土脂环酸芽孢杆菌在橙汁中的生长规律，利用近红外光谱获取橙汁中酸土脂环酸芽孢杆菌含量的信息，采用标准化（autoscale）、多元散射校正（multiplicative scatter correction，MSC）、标准正态变换（standard normal variate，SNV）、去趋势化（detrend）对光谱进行预处理，结合化学计量学，构建近红外光谱与酸土脂环酸芽孢杆菌含量预测模型。在此基础上，将近红外光谱转换为酸土脂环酸芽孢杆菌预测菌落数据，并采用“一步法”直接基于预测菌落数构建橙汁中酸土脂环酸芽孢杆菌的生长模型。结果表明，利用标准化进行光谱预处理建立的偏最小二乘（partial least squares，PLS）模型对橙汁中酸土脂环酸芽孢杆菌含量的预测效果相对较好，其预测决定系数（prediction determination coefficient，Rp2）与预测均方根误差（root mean square error of prediction，RMSEP）分别为0.733和0.242 lg CFU/mL，相对分析误差（relative percent deviation，RPD）为1.919。4种预测模型的均方误差（mean square error，MSE）介于0.0046~0.0300 lg CFU/mL之间；均方根误差（root mean square error，RMSE）介于为0.068~0.173 lg CFU/mL之间；赤池信息准则（akaike information criterion，AIC值）介于?66.383~?53.944之间，且Huang-full模型的3种指标相较更小，较适合描述橙汁中酸土脂环酸芽孢杆菌的生长。将近红外光谱获得预测菌落数构建的4种生长模型与平板计数法构建的生长模型分别进行相关性分析，发现4种模型的相关系数（r）均大于0.900，且Huang-full模型的拟合效果最优。所构建的模型通过准确因子（accuracy factor，
$ {A}_{f} $ ）进行验证，证实模型均具有良好的可靠性。因此，利用近红外光谱分析结合适当的化学计量学方法描述酸土脂环酸芽孢杆菌生长预测是可行的。
Abstract: Alicyclobacillus acidoterrestris is the dominant spoilage bacteria resulting the deterioration of orange juice. To simulate the growth of Alicyclobacillus acidoterrestris in orange juice, near-infrared (NIR) spectroscopy technique was used to predict the content of Alicyclobacillus acidoterrestri in orange juice. Different spectral pre-processing methods, including autoscale, multiplicative scatter correction (MSC), standard normal variate (SNV) and detrend, coupled with chemometric regression were used to build the prediction model of Alicyclobacillus acidoterrestris in orange juice by NIR spectroscopy. Based on that, the NIR predicted colony data of Alicyclobacillus acidoterrestris was used to develop the growth model of Alicyclobacillus acidoterrestris in orange juice by one-step approach. Results showed that, PLS model established by spectral pretreatment after Autoscale had relatively good prediction effect on the content of Alicyclobacillus acidoterrestri in orange juice, with the prediction determination coefficient (Rp2), root mean square error of prediction (RMSEP) and relative percent deviation (RPD) of 0.733, 0.242 lg CFU/mL and 1.919, respectively. Four different growth simulation models gave satisfactory predictions, with MSE values from 0.0046 to 0.0300 lg CFU/mL, RMSE values from 0.068 to 0.173 lg CFU/mL, AIC values from -66.383 to -53.944, respectively. Correlation analysis was performed between the four developed growth models based on the NIR prediction of colony number and the growth model constructed by plate counting method, and all of their correlation coefficients (r) were higher than 0.900. Particularly, the Huang-full model had the best ability to describe the growth of Alicyclobacillus acidoterrestris in orange juice and showed the best fitting results. Besides, the good reliability of all developed models was verified by accuracy factor ($ {A}_{f} $ ). Accordingly, this study indicated the potential to use NIR spectroscopy combined with advanced chemometrics to describe the growth prediction of Alicyclobacillus acidoterrestris in orange juice.
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图 1 三个温度下橙汁样品的透射近红外光谱图
Figure 1. Transmission near-infrared spectra of orange juice samples at three temperatures
图 2 Autoscale-PLS模型估计的酸土脂环酸芽孢杆菌含量测量值和预测值的散点图
Figure 2. Scatter plots of measured values and predicted values of Alicyclobacillus acidoterrestris contents estimated by Autoscale-PLS model
图 3 4种模型在不同温度条件下拟合的近红外光谱预测橙汁中酸土脂环酸芽孢杆菌的生长曲线
Figure 3. Growth curves of Alicyclobacillus acidoterrestris in orange juice predicted by NIR spectra under different temperature conditions by four models
表 1 三个温度的橙汁样品校正集与验证集的酸土脂环酸芽孢杆菌含量
Table 1. Content of Alicyclobacillus acidoterrestris in the calibration set and validation set of orange juice samples at three temperatures
样品集 样品数 酸土脂环酸芽孢杆菌含量（lg CFU/mL） 最小值 最大值 平均值 标准差 校正集 60 2.259 5.449 4.233 0.479 验证集 20 3.686 5.397 5.220 0.169 
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表 2 三个温度的橙汁样品中酸土脂环酸芽孢杆菌PLS模型的预测结果
Table 2. Results of PLS model for Alicyclobacillus acidoterrestris in orange juice samples at three temperatures
预处理方法 Rc2 RMSEC
（lg CFU/mL）Rp2 RMSEP
（lg CFU/mL）RPD None 0.875 0.226 0.677 0.269 1.726 Autoscale 0.878 0.224 0.733 0.242 1.919 MSC 0.893 0.209 0.649 0.289 1.604 SNV 0.893 0.209 0.649 0.289 1.603 Detrend 0.873 0.228 0.712 0.254 1.829 
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表 3 Huang-Full temperature range Ratkowsky模型参数
Table 3. Parameters of Huang-Full temperature range Ratkowsky model
参数 估计值 标准误差 t值 p值 置信下限 置信上限 评估值 MSE RMSE AIC r a 0.0285 69.3 0.00 1.00×10?0 ?154 155 0.0046 0.068 ?66.383 0.963 b 0.033 234 0.00 1.00×10?0 ?521 521 T0（°C） 10.3 2140 0.01 9.96×10?1 ?4760 4780 Tmax（°C） 58.4 67100 0.00 9.99×10?1 ?150000 150000 A ?4.07 41.5 ?0.10 9.24×10?1 ?96.6 88.4 m 2.09 11.3 0.18 8.57×10?1 ?23 27.2 Y0,4（lg CFU/mL） 3.37 0.991 3.40 6.72×10?3 1.17 5.58 Y0,20（lg CFU/mL） 3.81 0.936 4.07 2.24×10?3 1.73 5.9 Y0,40（lg CFU/mL） 4.22 0.882 4.79 7.35×10?4 2.26 6.19 ymax（lg CFU/mL） 5.41 1.22 4.45 1.24×10?3 2.7 8.11 
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表 4 No-lag phase-Suboptimal Ratkowsky模型参数
Table 4. Parameters of No-lag phase-Suboptimal Ratkowsky model
参数 估计值 标准误差 t值 p值 置信下限 置信上限 评估值 MSE RMSE AIC r a 0.019 0.00626 3.03 8.95×10?3 0.00556 0.0324 0.0300 0.173 ?53.944 0.907 T0（°C） 11.2 2.46 4.56 4.45×10?4 5.94 16.5 Y0,4（lg CFU/mL） 3.2 0.737 4.34 6.83×10?4 1.62 4.78 Y0,20（lg CFU/mL） 3.85 0.721 5.34 1.04×10?4 2.3 5.4 Y0,40（lg CFU/mL） 3.98 0.644 6.19 2.37×10?5 2.6 5.36 ymax（lg CFU/mL） 5.35 0.566 9.46 1.85×10?7 4.14 6.56 
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表 5 No-lag phase-Full temperature range Ratkowsky模型参数
Table 5. Parameters of No-lag phase-Full temperature range Ratkowsky model
参数 估计值 标准误差 t值 p值 置信下限 置信上限 评估值 MSE RMSE AIC r a 0.026 96.3 0.00 1.00×10?0 ?210 210 0.0131 0.115 ?60.915 0.920 b 0.0332 370 0.00 1.00×10?0 ?807 807 T0（°C） 10.3 3270 0.00 9.98×10?1 ?7110 7130 Tmax（°C） 59.2 112000 0.00 1.00×10?0 ?243000 244000 Y0,4（lg CFU/mL） 3.17 0.749 4.24 1.15×10?3 1.54 4.8 Y0,20（lg CFU/mL） 3.72 0.775 4.81 4.29×10?4 2.04 5.41 Y0,40（lg CFU/mL） 4.21 0.735 5.73 9.53×10?5 2.61 5.81 ymax（lg CFU/mL） 5.47 1.82 3.01 1.09×10?2 1.51 9.43 
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表 6 Huang-Suboptimal Ratkowsky模型参数
Table 6. Parameters of Huang-Suboptimal Ratkowsky model
参数 估计值 标准误差 t值 p值 置信下限 置信上限 评估值 MSE RMSE AIC r a 0.022 0.00923 2.39 3.44×10?2 0.00191 0.0421 0.0106 0.103 ?65.168 0.949 T0（°C） 10.7 2.31 4.64 5.72×10?4 5.68 15.8 A 1.16 2.04 0.57 5.79×10?1 ?3.28 5.61 m 0.602 1.15 0.52 6.10×10?1 ?1.9 3.11 Y0,4（lg CFU/mL） 3.3 0.876 3.77 2.68×10?3 1.39 5.21 Y0,20（lg CFU/mL） 3.89 0.871 4.46 7.76×10?4 1.99 5.78 Y0,40（lg CFU/mL） 4.27 0.706 6.04 5.86×10?5 2.73 5.8 ymax（lg CFU/mL） 5.33 0.543 9.82 4.37×10?7 4.15 6.51 
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表 7 4种模型在不同温度条件下的验证结果
Table 7. Validation results of four models under different temperature conditions
模型 $ {A}_{f} $ $ {B}_{f} $ 4°C 20°C 40°C 4°C 20°C 40°C Huang-full 1.056 1.020 1.018 1.038 0.981 0.985 No-sub 1.090 1.033 1.043 1.063 0.983 0.992 No-full 1.103 1.017 1.014 1.074 0.995 0.991 Huang-sub 1.062 1.037 1.034 1.044 0.971 0.978 
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[1] 高雯. 浓缩果汁中嗜酸耐热菌和耐热霉菌的检测与研究[D]. 武汉: 武汉轻工大学, 2016: 1−5.GAO W. Detection and study of Alicyclobacillus spp. and heat resistant mould from the concentrated fruit juice[D]. Wuhan: Wuhan Polytechnic University, 2016: 1−5. [2] 姚培鑫, 马小魁. 浓缩果汁生产厂中嗜酸耐热菌的跟踪检测[J]. 饮料工业,2001,4(3):42?44. [YAO P X, MA X K. Online exploration of Thermophilic acidophilic bacteria in fruit juice concentrate manufacturers[J]. The Beverage Industry,2001,4(3):42?44. doi: 10.3969/j.issn.1007-7871.2001.03.014 [3] 黄玉鑫, 冯鑫, 史文鑫, 等. 酸土脂环酸芽孢杆菌危害及其控制研究进展[J]. 食品工业科技,2019,40(12):347?353. [HUANG Y X, FENG X, SHI W X, et al. Research progress on harm and hazard control of Alicyclobacillus acidoterrestris[J]. Science and Technology of Food Industry,2019,40(12):347?353. doi: 10.13386/j.issn1002-0306.2019.12.056 [4] 王周利, 蔡瑞, 岳田利, 等. 果汁中脂环酸芽孢杆菌识别与控制研究进展[J]. 农业机械学报,2016,47(10):221?243. [WANG Z L, CAI R, YUE T L, et al. Review of identification and control technology of Alicyclobacillus spp. in fruit juice[J]. Transactions of the Chinese Society of Agricultural Machinery,2016,47(10):221?243. doi: 10.6041/j.issn.1000-1298.2016.10.029 [5] 张晨星, 崔丽佼, 于有伟, 等. 浓缩苹果汁中酸土脂环酸芽孢杆菌检测及控制措施研究进展[J]. 中国果菜,2021,41(1):33?42. [ZHANG C X, CUI L J, YU Y W, et al. Advances in the detection and control of Alicyclobacillus spp. in apple juice[J]. China Fruit and Vegetable,2021,41(1):33?42. doi: 10.19590/j.cnki.1008-1038.2021.01.006 [6] 唐翠娥, 彭丽桃, 李颉, 等. 橙汁主要营养成分及温度处理对脂环酸芽胞杆菌生长的影响[J]. 食品工业科技,2013,34(10):299?303. [TANG C E, PENG L T, LI J, et al. Effect of nutritional ingredient and temperature-treatment of orange juice on the growth of Alicyclobacillus[J]. Science and Technology of Food Industry,2013,34(10):299?303. doi: 10.13386/j.issn1002-0306.2013.10.066 [7] 徐茜茜. 酸土脂环酸芽孢杆菌（Alicyclobacillus acidoterrestris）芽孢形成及低pH条件下芽孢萌发的蛋白组学研究[D]. 新乡: 河南科技学院, 2017: 1−5.XU X X. Study on spore formation and proteomics of spore germination at low pH in Alicyclobacillus acidoterrestris[D]. Xinxiang: Henan Institute of Science and Technology, 2017: 1−5. [8] EMANUELA C, ANTONIO B, MILENA S, et al. Alicyclobacillus spp. : New insights on ecology and preserving food quality through new approaches[J]. Microorganisms,2015,3(4):625?640. doi: 10.3390/microorganisms3040625 [9] ORR R, SHEWFELT R, HUANG C, et al. Detection of guaiacol produced by Alicyclobacillus acidoterrestris in apple juice by sensory and chromatographic analyses, and comparison with spore and vegetative cell populations[J]. Journal of Food Protection,2000,63(11):1517?1522. doi: 10.4315/0362-028X-63.11.1517 [10] MCDONALD K, SUN D. Predictive food microbiology for the meat industry: A review[J]. International Journal of Food Microbiology,1999,52(1-2):1?27. doi: 10.1016/s0168-1605(99)00126-9 [11] 刘亚兵, 何腊平, 高泽鑫, 等. 食品微生物生长预测模型的研究[J]. 食品工业,2016,37(11):159?164. [LIU Y B, HE L P, GAO Z X, et al. Research on predictive model of food microorganism growth[J]. The Food Industry,2016,37(11):159?164. [12] GONZALEZ C, POSSAS A, CARRASCO E, et al. 'MicroHibro': A software tool for predictive microbiology and microbial risk assessment in foods[J]. International Journal of Food Microbiology,2019,290:226?236. doi: 10.1016/j.ijfoodmicro.2018.10.007 [13] WHITING R, BUCHANAN R. A classification of models for predictive microbiology[J]. Food Microbiology,1993,10(4):175?177. [14] 姬华. 对虾中食源性弧菌预测模型建立及风险评估[D]. 无锡: 江南大学, 2012: 6-8JI H. Establishment of predictive model and risk assessment for foodborne Vibrio spp. in Shrimp[D]. Wuxi: Jiangnan University, 2012: 6-8 [15] 牛犇, 洪斌, 穆丼丼, 等. 荧光定量PCR在预测微生物学中的应用[J]. 食品工业科技,2019,40(421):281?288. [NIU B, HONG B, MU L L, et al. Application of fluorescent quantitative PCR in predictive microbiology[J]. Science and Technology of Food Industry,2019,40(421):281?288. doi: 10.13386/j.issn1002-0306.2019.05.046 [16] 刘珊娜. 李斯特菌生长预测模型的研究进展[J]. 天津农学院学报,2018,25(3):92?95. [LIU S N. Research progress in predictive models for Listeria growth[J]. Journal of Tianjin Agricultural University,2018,25(3):92?95. doi: 10.19640/j.cnki.jtau.2018.03.020 [17] GILL C, GREER G, DILTS B. The aerobic growth of Aeromonas hydrophila and Listeria monocytogenes in broths and on pork[J]. International Journal of Food Microbiology,1997,35:67?74. doi: 10.1016/S0168-1605(96)01224-X [18] 褚小立, 陈瀑, 李敬岩, 等. 近红外光谱分析技术的最新进展与展望[J]. 分析测试学报,2020,39(10):1181?1188. [CHU X L, CHENG P, LI J Y, et al. Progresses and perspectives of near infrared spectroscopy analytical technology[J]. Journal of Instrumental Analysis,2020,39(10):1181?1188. doi: 10.3969/j.issn.1004-4957.2020.10.001 [19] 周冰谷, 花振新, 杨荣, 等. 近红外光谱技术在食品微生物检测中的应用[J]. 食品安全质量检测学报,2019,10(16):5393?5398. [ZHOU B G, HUA Z X, YANG R, et al. Application of near-infrared spectroscopy in food microorganism detection[J]. Food Safety and Quality Detection Technology,2019,10(16):5393?5398. doi: 10.3969/j.issn.2095-0381.2019.16.028 [20] 赵政. 猪肉新鲜度光谱模型的建立及传递方法研究[D]. 武汉: 华中农业大学, 2013.ZHAO Z. The Establishment and transfer of the spectral model for pork freshness detection[D]. Wuhan: Huazhong Agricultural University, 2013. [21] 刘秋安, 徐芳芳, 张欣, 等. 基于近红外光谱技术和分类与回归树算法建立天舒片崩解时间预测模型[J]. 中草药,2021,52(16):4837?4843. [LIU Q A, XU F F, ZHANG X, et al. Based on near-infrared spectroscopy technology and classification and regression trees algorithm to establish a prediction model of Tianshu Tablets disintegration[J]. Chinese Traditional and Herbal Drugs,2021,52(16):4837?4843. doi: 10.7501/j.issn.0253-2670.2021.16.008 [22] 王健健. 基于傅立叶近红外光谱分析技术的南疆“温185”核桃综合预测模型研究[D].阿拉尔: 塔里木大学, 2020.WANG J J. Study on the comprehensive prediction model of ''Wen 185'' walnut in south xinjiang based on fourier near infrared spectroscopy[D]. Alaer: Tarim University, 2020. [23] 崔蕴涵, 许金钗, 方智毅, 等. 莲子含水率近红外光谱检测模型构建[J]. 食品研究与开发,2021,42(18):130?135. [CUI Y H, XU J C, FANG Z Y, et al. Detection model of lotus seed water content based on near-infrared spectroscopy[J]. Food Research and Development,2021,42(18):130?135. doi: 10.12161/j.issn.1005-6521.2021.18.019 [24] Simen A, Philippe N, Van I. A tutorial on uncertainty propagation techniques for predictive microbiology models: A critical analysis of state-of-the-art techniques[J]. International Journal of Food Microbiology,2018,282:1?8. doi: 10.1016/j.ijfoodmicro.2018.05.027 [25] HUANG L. Growth kinetics of Listeria monocytogenes in broth and beef frankfurters: determination of lag phase duration and exponential growth rate under isothermal conditions[J]. Journal of Food Science,2008,73(5):E235?E242. doi: 10.1111/j.1750-3841.2008.00785.x [26] HUANG L. Optimization of a new mathematical model for bacterial growth[J]. Food Control,2013,32(1):283?288. doi: 10.1016/j.foodcont.2012.11.019 [27] FANG T, GURTLER J, HUANG L. Growth kinetics and model comparison of Cronobacter sakazakii in reconstituted powdered infant formula[J]. Journal of Food Science,2012,77(7-8-9):E247?E255. doi: 10.1111/j.1750-3841.2012.02873.x [28] FANG T, LIU Y, HUANG L. Growth kinetics of Listeria monocytogenes and spoilage microorganisms in fresh-cut cantaloupe[J]. Food Microbiology,2013,34(1):174?181. doi: 10.1016/j.fm.2012.12.005 [29] RATKOWSKY D, MCMEEKIN T, BALL A. Relationship between temperature and growth rate of bacterial cultures[J]. Journal of Bacteriology,1982,154(1):1222?1226. doi: 10.1128/jb.149.1.1-5.1982 [30] RATKOWSKY D, LOWRY R, MCMEEKIN T, et al. Model for bacterial culture growth rate throughout the entire biokinetic temperature range[J]. Journal of Bacteriology,1983,154(3):1222?1226. doi: 10.1128/jb.154.3.1222-1226.1983 [31] FENG C, DRUMMOND L, SUN D. Modelling the growth parameters of lactic acid bacteria and total viable count in vacuum-packaged Irish cooked sausages cooled by different methods[J]. International Journal of Food Science & Technology,2014,49(12):2659?2667. doi: 10.1111/ijfs.12603 [32] 张玉华, 孟一, 郭风军, 等. 基于预测微生物学的冷却牛肉货架期预测模型的建立[J]. 肉类研究,2019,33(11):61?66. [ZHANG Y H, MENG Y, GUO F J, et al. Predictive modelling for shelf-life determination of chilled beef based on predictive microbiology[J]. Meat Research,2019,33(11):61?66. doi: 10.7506/rlyj1001-8123-20190819-186 [33] 朱金艳, 朱玉杰, 冯国红, 等. 基于近红外光谱技术联合极限学习机的蓝莓贮藏品质定量模型建立[J]. 食品与发酵工业,2022,48(16):270?276. [ZHU J Y, ZHU Y J, FENG G H, et al. Establishment of quantitative models for blueberry storage quality based on near infrared spectroscopy combined with extreme learning machine[J]. Food and Fermentation Industries,2022,48(16):270?276. doi: 10.13995/j.cnki.11-1802/ts.029223 [34] 王军. 苹果汁中嗜酸耐热菌免疫分离及振动光谱法鉴定[D]. 咸阳: 西北农林科技大学, 2011: 50−53.WANG J. Immunomagnetic separation of Alicyclobacillus species in apple juice and identification by vibrational spectroscopy[D]. Xianyang: Northwest A&F University, 2011: 50−53. [35] 杨康. 源于苹果汁的嗜酸耐热菌生长代谢动力学及红外光谱快速检测技术研究[D]. 咸阳: 西北农林科技大学, 2007.YANG K. Study on kinetics and FT-IR rapid detection of Alicyclobacillus acidoterrestris originated from apple juice[D]. Xianyang: Northwest A&F University, 2007. [36] MENGSHI LIN A, MURAD AL-HOLY B, SU-SEN CHANG A, et. al. Rapid discrimination of Alicyclobacillus strains in apple juice by Fourier transform infrared spectroscopy[J]. Food Microbiology,2005,105(3):369?376. doi: 10.1016/j.ijfoodmicro.2005.04.018 [37] 张亮. 扩展青霉和展青霉素的近红外检测技术研究[D]. 咸阳: 西北农林科技大学, 2010.ZHANG L. Application of near infrared spectroscopy on detection of penicillium expansum and patulin[D]. Xianyang: Northwest A&F University, 2010. [38] WALLS I, CHUYATE R. Alicyclobacillus: Historical perspective and preliminary characterization study[J]. Dairy Food & Environmental Sanitation A Publication of the International Association of Milk Food & Environmental Sanitarians,1998,18(8):499?503. [39] 冯鑫, 何承云, 徐茜茜, 等. 培养基成分对酸土脂环酸芽孢杆菌生长及芽孢形成的影响[J]. 食品工业科技,2018,39(19):84?89. [FENG X, HE C Y, XU X X, et al. Effect of medium component on growth and spore formation of Alicyclobacillus acidoterrestris[J]. Science and Technology of Food Industry,2018,39(19):84?89. doi: 10.13386/j.issn1002-0306.2018.19.015 -
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