Impact of maturity and variety on the hyperspectral detection model for determining soluble solid content in fresh apricots
DOI:
https://doi.org/10.52151/jae2024616.1899Keywords:
Fresh apricot, Soluble solid content, Mahalanobis distance, Model updating, radial basis function, standard normal variate transformation, successive projection algorithmAbstract
The hyperspectral technique is a non-destructive method for quantifying the organic matter content, and has emerged as a prevalent approach for measuring soluble solid content (SSC) in fruits. However, existing hyperspectral models often face challenges in accurately predicting new samples of different maturities and varieties, thus limiting their wide application. The aim of this study was to investigate the effects of maturity and variety on the hyperspectral detection model for SSC of fresh apricot, and to establish a robust global model. Standard normal variable transformation method was used to reduce the influence of uneven light distribution on spherical fruit. There are four data sets: 6-1-pre-ripe stage, 6-1-ripening stage, Wanghong-ripening stage, and Jinmei-ripening stage. Through the Mahalanobis distance-concentration gradient (MD-CG) method, representative samples (5, 10, 15) were selected from the fresh apricot data set of other maturity and varieties as representative and typical sample points, and included in the training set for model update. This greatly improves the stability of the model. By integrating representative samples from the other three datasets into the 6-1-ripening (6-1-R) training set, combined with feature wavelength selection and radial basis function neural network updates, the global model effectively predicted SSC (RPD > 1.4) for the remaining three categories of fresh apricot. The global model is mainly affected by the variation of fresh apricot varieties, and the influence of maturity on the model accuracy is relatively small. The results show that using MD-CG recalibration model as a rapid detection strategy can determine the content of SSC in fresh apricots of different maturity or varieties, thus providing a solid foundation for its industrial application.
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Anderson, N. T., Walsh, K. B., Flynn, J. R., & Walsh, J. P. (2021). 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 Biology and Technology, 171, 111358. https://doi.org/10.1016/j.postharvbio.2020.111358.
Benelli, A., Cevoli, C., Ragni, L., Fabbri, A. (2021). In-field and non-destructive monitoring of grapes maturity by hyperspectral imaging, Biosystems Engineering, 207, 59-67, https://doi.org/10.1016/j.biosystemseng.2021.04.006
Chen, J., Yu, H., Jiang, D., Zhang, Y., & Wang, K. (2022). A novel NIRS modelling method with OPLS-SPA and MIX-PLS for timber evaluation. Journal of Forestry Research, 33, 369–376). https://doi.org/10.1007/S11676-021-01314-Y
Feng, C., Arai, H., & Francisco J. (2023). Hyperspectral imaging combined with chemometrics analysis for monitoring the textural properties of modified casing sausages with differentiated additions of orange extracts. Foods, 12(5), 1069. https://doi.org/10.3390/foods12051069
Gao, L., Lu, C., Guo, G., Zhang, X., & Lin, S. (2022). Quantum K-nearest neighbors classification algorithm based on Mahalanobis distance. Frontiers in Physics, 10, 1047466. https://doi.org/10.3389/fphy.2022.1047466
Gao, S., & Xu, J. (2022). Hyperspectral image information fusion-based detection of soluble solids content in red globe grapes. Computers and Electronics in Agriculture, 196, 106822. https://doi.org/10.1016/j.compag.2022.106822
Guo, P., Shi, Z., Li, M., Luo, W., & Cha, Z. (2023). Estimating foliar phosphorus of rubber trees using locally modelling approach with hyperspectral reflectance. Infrared Physics and Technology, 131, 104642. https://doi.org/10.1016/J.INFRARED.2023.104642
Hou, L., & Gao, J. (2019). Forecasting of short-term load based on LMD and BBO-RBF model. International Journal of Plant Engineering and Management, 24(02),101-108. https://doi.org/10.13434/j.cnki.1007-4546.2019.0205
Huang, X., Xie, Y., Bao, Y. & Bao, Y. (2019). Estimation of leaf loss rate in larch infested with Erannis Jacobsoni Djak based on differential spectral continuous wavelet coefficient. Spectroscopy and Spectral Analysis, 39(09), 2732-2738. https://doi.org/10.3964/j.issn.1000-0593(2019)09-2732-07
Jia, W., Liang, G., Tian, H., & Wan, C. (2019). Electronic nose-based technique for rapid detection and recognition of moldy apples. Sensors, 19(7), 1526. https://doi.org/10.3390/s19071526
Jiang, X., Zhu, M., Yao, J., Zhang, Y., & Liu, Y. (2022). Study on the effect of apple size difference on soluble solids content model based on near-infrared (NIR) spectroscopy. Journal of Spectroscopy, 2022, 3740527, https://doi.org/10.1155/2022/3740527
Li, J., Zhang, H., Zhan, B., & Jiang, Y. (2019). Determination of SSC in pears by establishing the multi-cultivar models based on visible-NIR spectroscopy. Infrared Physics and Technology, 102, 103066. https://doi.org/10.1016/j.infrared.2019.103066
Li, Y., & Yang, X. (2023). Quantitative analysis of near infrared spectroscopic data based on dual-band transformation and competitive adaptive reweighted sampling. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 285, 121924. https://doi.org/10.1016/J.SAA.2022.121924
Liu, Y., Barton, F. E., Lyon, B. G., William, W. R., & Lyon, C. E. (2004). Two-dimensional correlation analysis of visible/near-infrared spectral intensity variations of chicken breasts with various chilled and frozen storages. Journal of Agricultural and Food Chemistry, 52(3), 505-510. https://doi.org/10.1021/jf0303464
Lu, H., Zhang, J., Li, L., Liu, Z.., Yang, H., Feng, Y., & Yin, L. (2021). Least angle regression combined with competitive adaptive re-weighted sampling for NIR spectral wavelength selection. Spectroscopy and Spectral Analysis, 41(6), 1782-1788 https://doi.org/10.3964/j.issn.1000-0593(2021)06-1782-07
Manuela, M., Luca, M., Rohullah, Q., Elena, L., Virginia, T., Francesco, G., & Bruno, M. (2023). Prediction of soluble solids content by means of NIR spectroscopy and relation with botrytis cinerea tolerance in strawberry cultivars. Horticulturae, 9(1),91. https://doi.org/10.3390/HORTICULTURAE9010091
Mitrofanova, I. V., Mitrofanova, O. V., Lesnikova-Sedoshenko, N. P., Chelombit, S. V., Gorina V. M., & Chirkov, S. N. (2020). Some features of obtaining new breeding forms of apricot in vitro. ISHS Acta Horticulturae 1290: XVII International Symposium on Apricot Breeding and Culture, 237-242. https://doi.org/10.17660/ACTAHORTIC.2020.1290.42
Noel, S. J., Jørgensen, H. J., & Bach, K. E. (2021). Prediction of protein and amino acid composition and digestibility in individual feedstuffs and mixed diets for pigs using near-infrared spectroscopy. Animal Nutrition, 7(4), 1242-1252. https://doi.org/10.1016/J.ANINU.2021.07.004
Piazzolla, F., Amodio, L. M., & Colelli, G. (2017). Spectra evolution over on-vine holding of Italia table grapes: prediction of maturity and discrimination for harvest times using a Vis-NIR hyperspectral device. Journal of Agricultural Engineering, 48(2),109-116. https://doi.org/10.4081/jae.2017.639
Pratiwi, D., Pahlawan, F. R., Rahmi, N., Amanah, Z. & Masithoh, E. (2023). Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible–shortwave near-infrared spectroscopy. Open Agriculture, 8(1), 20220183. https://doi.org/10.1515/OPAG-2022-0183
Rahim, A., Ali R., Bahareh J., & Mahmoud, O. (2023). New approach for rapid estimation of leaf nitrogen, phosphorus, and potassium contents in apple-trees using Vis/NIR spectroscopy based on wavelength selection coupled with machine learning. Computers and Electronics in Agriculture, 207, 107746. https://doi.org/10.1016/J.COMPAG.2023.107746
Rodriguez-Gomez, C., Kereszturi, G., Jeyakumar, P., Pullanagari, R., Reeves, R., Rae, A., & . Procter, J. N. (2023). Remote exploration and monitoring of geothermal sources: A novel method for foliar element mapping using hyperspectral (VNIR-SWIR) remote sensing. Geothermics, 111, 102716. https://doi.org/10.1016/j.geothermics.2023.102716
Romaniello, R., Peri, G., & Leone, A. (2016). Fluorescence hyper-spectral imaging to detecting faecal contamination on fresh tomatoes. Journal of Agricultural Engineering, 47(1),7-11. https://doi.org/10.4081/jae.2016.491
Stanislaw, G., & Wojciech, G. (2022). Assessing the information potential of MIR spectral signatures for prediction of multiple soil properties based on data from the AfSIS phase I project. International Journal of Environmental Research and Public Health, 19(22), 15210. https://doi.org/10.3390/IJERPH192215210
Wang, D., Wei, W., Lai, Y., Yang, X., Li, S., Jia, L., & Wu, D. (2019). Comparing the potential of near- and mid-infrared spectroscopy in determining the freshness of strawberry powder from freshly available and stored strawberry. Journal of Analytical Methods in Chemistry, 2019, 2360631. https://doi.org/10.1155/2019/2360631
Wang, X., Yang, H., Li, X.., Zheng, Y., Yan, H., & Li, N. (2021). Research on maize growth monitoring based on visible spectrum of UAV remote sensing. Spectroscopy and Spectral Analysis,41(1),265-270. https://doi.org/ 10.3964/j.issn.1000-0593(2021)01-0265-06
Wei, K., Ma, C., Sun, K., Liu, Q., Zhao, N., Sun, Y., Tu, K., & Pan, L. (2020). Relationship between optical properties and soluble sugar contents of apple flesh during storage. Postharvest Biology and Technology, 159, 111021, https://doi.org/10.1016/j.postharvbio.2019.111021
Wu, Q., Oliveira, M., Achata, M., & Kamruzzaman, M. (2023). Reagent-free detection of multiple allergens in gluten-free flour using NIR spectroscopy and multivariate analysis. Journal of Food Composition and Analysis, 120, 105324. https://doi.org/10.1016/J.JFCA.2023.105324
Xiong, Y., Ohashi, S., Nakano, K., Jiang, W., Takizawa, K., Iijima, K., & Maniwara, P. (2021). Application of the radial basis function neural networks to improve the nondestructive Vis/NIR spectrophotometric analysis of potassium in fresh lettuces. Journal of Food Engineering, 298, 110417. https://doi.org/10.1016/J.JFOODENG.2020.110417
Yang, Y., Sun, D., Pu, H., & Zhu, Z. (2015). Rapid detection of anthocyanin content in lychee pericarp during storage using hyperspectral imaging coupled with model fusion. Postharvest Biology and Technology, 103, 55-65. https://doi.org/10.1016/j.postharvbio.2015.02.008
Yin, L., Lv, L., Wang, D., Qu, Y., Chen, H., & Deng, W. (2023). Spectral clustering approach with K-nearest neighbor and weighted Mahalanobis distance for data mining. Electronics, 12(15), 3284. https://doi.org/10.3390/ELECTRONICS12153284
Yu, X., Lu, H., & Wu, D. (2018). Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging. Postharvest Biology and Technology, 141, 39-49. https://doi.org/10.1016/j.postharvbio.2018.02.013
Zhang, C., Shi, Y., Wei, Z., Wang, R., Li, T., Wang, Y., & Gu, X. (2022a). Hyperspectral estimation of the soluble solid content of intact netted melons decomposed by continuous wavelet transform. Frontiers in Physics, 10, 1034982. https://doi.org/10.3389/FPHY.2022.1034982.
Zhang, Y., Huang, J., Zhang, Q., Liu, J., Meng, Y., & Yu, Y. (2022b). Nondestructive determination of SSC in an apple by using a portable near-infrared spectroscopy system. Applied optics,61(12), 3419-3428. https://doi.org/10.1364/AO.455024.
Zheng, Y., Liu, P., Zheng, Y., & Xie, L. (2024). Improving SSC detection accuracy of cherry tomatoes by feature synergy and complementary spectral bands combination. Postharvest Biology and Technology, 213, 112922-. https://doi.org/10.1016/J.POSTHARVBIO.2024.112922
Zheng, Y., Tian, S., & Xie, L. (2023). Improving the identification accuracy of sugar orange suffering from granulation through diameter correction and stepwise variable selection. Postharvest Biology and Technology, 200, 112313 https://doi.org/10.1016/J.POSTHARVBIO.2023.112313
Zhu, P., Yang, Q., & Zhao, H. (2022). Identification of peanut oil origins based on Raman spectroscopy combined with multivariate data analysis methods. Journal of Integrative Agriculture, 21(9), 2777-2785. https://doi.org/10.1016/J.JIA.2022.07.026





