Prediction of Apple Hybrid Offspring Aroma Based on Hyperspectral

• Published in: Foods Vol. 11; no. 23; p. 3890
• Main Authors: Zhu, Huili, Wang, Minyan, Zhang, Jing, Ma, Fengwang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02.12.2022; MDPI
• Subjects: Alcohols; Aldehydes; Algorithms; Apple; Apples; Aroma compounds; Cameras; Cultivars; Food science; Fruits; Gas chromatography; HSI; hybrid offspring; hyperspectral; Hyperspectral imaging; Ketones; Mass spectrometry; Mass spectroscopy; Model accuracy; Offspring; Physiological aspects; Prediction models; Preprocessing; Quality standards; random forest algorithm; Root-mean-square errors; Search algorithms; Software; Spectra; Standardization; Wavelengths; China; United States > US; apple; random forest algorithm; hybrid offspring; hyperspectral; HSI
• ISSN: 2304-8158; 2304-8158
• DOI: 10.3390/foods11233890

Used Random forest algorithm to construct a prediction model of aroma components based on the hybrid offspring of ‘Honeycrisp’ × ‘Maodi’, and different preprocessing methods were tried (Standardization (SS), First-order Derivative (D1) and Standard normal variate (SNV)). The aroma composition and content were determined by gas chromatography-mass spectrometry (GC-MS), and the main aroma components of apples were classified according to compound categories, including ester, aldehyde, ketone, alcohol. Taking the chemical groups as the research objects, the characteristic wavelengths were selected by grid search algorithm, and the characteristic wavelength-aroma chemical group model was established, and the same method was used to construct the model for single aroma components. The results show: SNV has the best noise removal effect among the five preprocessing methods. Under the SNV treatment, aroma chemical groups of apples showed a good correlation with the spectrum. The number of characteristic spectra of ester are 413, 493, 512, 551, 592, 600, 721, 727, 729, 733 nm, all in the visible light range. The determination coefficient (R2), the root mean square error (RMSE) and the ratio of the standard deviation values (RPD) of validation were 0.90, 4936.16 and 1.13. The characteristic spectrum of alcohols is 519, 562, 570, 571, 660, 676, 737, 738 nm, the range is close to that of ester. The R2 and RMSE of alcohol validation are 0.92 and 83.21, and RPD is 1.30. The number of characteristic spectra of aldehyde is 20, and the most important band is 1000 nm, which is outside the visible light range. The number of characteristic spectra of ketone is 15, and also has some distribution outside the visible light range. The R2 of aldehyde and ketone validation are 0.84 and 0.86. Except for cyclooctanol, the R2 of single aroma compound prediction model performed poorly. Based on the models, we tried to visualize alcohol, which can roughly represent their distribution on apple. Their distributions all show significant differences in the center and edge of apple, but the results are still rough due to the accuracy of models. In conclusion, the study can preliminarily prove that hyperspectral imaging technology (HSI) can perform non-destructive detection of aroma in apple hybrid offspring.