Vis/NIR optical biosensors applications for fruit monitoring

被引：0
作者：
Wang M. [1 ]
Xu Y. [1 ]
Yang Y. [1 ]
Mu B. [1 ]
Nikitina M.A. [2 ]
Xiao X. [1 ]
机构：
[1] Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing
[2] V.M. Gorbatov Federal Research Center for Foods Systems of RAS, 26, Talalikhina Str., Moscow
来源：
Biosensors and Bioelectronics: X | 2022年 / 11卷
关键词：
Chemometrics; Non-destructive evaluation; Prediction models; Vis/NIR spectroscopy;
D O I：
10.1016/j.biosx.2022.100197
中图分类号：
学科分类号：
摘要：
The global fruit industry is continually confronted with new technological challenges to meet people's material quality of life expectations. Fruit maturity has a strong association with the receiving time, transportation technique, and storage method of the fruit, and it has a direct impact on the fruit's quality. How to undertake rapid and non-destructive fruit quality testing has become a prominent topic in recent years. Because of its high repeatability, ease of operation, pollution-free, and measurement stability, visible and near-infrared (Vis/NIR) spectroscopy has become the most advanced non-destructive quality assessment technique in terms of equipment, applications, and data analysis methods in the field of non-destructive monitoring. An overview of the use of Vis/NIR optical biosensors in fruit internal quality monitoring and variety identification is presented. The benefits and drawbacks of various types of optical biosensors, as well as the practicality of various measurement modalities, are explored. Commonly used spectral biosensor data processing methods are summarized, including preprocessing, variable selection, calibration, and validation. Finally, the transition of pricey handheld NIR equipment to more cost-effective photodiode-based fruit maturity estimate devices was indicated as an issue for further investigation. © 2022 The Authors
引用
收藏
相关论文
共 138 条
  • [1] Abasi S., Minaei S., Jamshidi B., Fathi D., Khoshtaghaza M.H., Rapid measurement of apple quality parameters using wavelet de-noising transform with Vis/NIR analysis, Sci. Hortic., 252, pp. 7-13, (2019)
  • [2] Aggarwal S., Mohite A.M., Sharma N., The maturity and ripeness phenomenon with regard to the physiology of fruits and vegetagles: a review, Bull. Transilv. Univ. Brasov, Ser. II For. Wood Ind. Agric. Food Eng., 11, pp. 77-88, (2018)
  • [3] Ali M.M., Janius R.B., Nawi N.M., Hashim N., Prediction of total soluble solids and pH in banana using near infrared spectroscopy, J. Eng. Sci. Technol., 13, pp. 254-264, (2018)
  • [4] Anderson N.T., Walsh K.B., Subedi P.P., Hayes C.H., Achieving robustness across season, location and cultivar for a NIRS model for intact mango fruit dry matter content, Postharvest Biol. Technol., 168, (2020)
  • [5] Anderson N.T., Walsh K.B., Flynn J.R., Walsh J.P., Achieving robustness across season, location and cultivar for a NIRS model for intact mango fruit dry matter content. II. Local PLS and nonlinear models, Postharvest Biol. Technol., 171, (2021)
  • [6] Antonucci F., Pallottino F., Paglia G., Palma A., D'Aquino S., Menesatti P., Non-destructive estimation of Mandarin maturity status through portable VIS-NIR spectrophotometer, Food Bioprocess Technol., 4, pp. 809-813, (2011)
  • [7] Araujo M.C.U., Saldanha T.C.B., Galvao R.K.H., Yoneyama T., Chame H.C., Visani V., The successive projections algorithm for variable selection in spectroscopic multicomponent analysis, Chemometr. Intell. Lab. Syst., 57, pp. 65-73, (2001)
  • [8] Barnaba F.E., Bellincontro A., Mencarelli F., Portable NIR-AOTF spectroscopy combined with winery FTIR spectroscopy for an easy, rapid, in-field monitoring of Sangiovese grape quality, J. Sci. Food Agric., 94, pp. 1071-1077, (2014)
  • [9] Barnes R.J., Dhanoa M.S., Lister S.J., Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra, Appl. Spectrosc., 43, pp. 772-777, (1989)
  • [10] Basile T., Marsico A.D., Perniola R., NIR analysis of intact grape berries: chemical and physical properties prediction using multivariate analysis, Foods, 10, (2021)
12345678910