The traditional detection of the mineral nutrient content is mostly based on laboratory physical and chemical analysis, including inductively coupled plasma mass spectrometry (ICP-MS), atomic absorption spectrometry, UV-visible spectrophotometer measurement and so on. A large number of studies have proved that the content of calcium, boron and potassium may be important factors affecting the appearance quality of bagged ‘Huangguan’ pears. However, brown spots that often appear on the surface of bagged ‘Huangguan’ pears have been seriously affecting the appearance quality and economic benefits of the fruits.
In actual production, ‘Huangguan’ pears are often bagged to prevent pests and diseases.
‘Huangguan’ pears ( Pyrus bretschneideri Rehd cv.Huangguan), which are famous for their excellent quality in Chinese and even the world’s pear market, have been introduced and planted in many places with good performance and have a good development prospect. The results also provide an important scientific theoretical basis for further research on the degradation of the quality of ‘Huangguan’ pears caused by a lack of nutrients. Therefore, the micro near-infrared spectrometer based on machine learning can predict the content of calcium, boron and potassium in the pulp and peel of ‘Huangguan’ pears accurately and quickly. According to the prediction results, the value of the determination coefficient of the prediction sets of the best prediction models for the three elements all reached ideal levels, and the values of their Relative analysis error also showed high levels. Finally, the characteristic wavelengths were extracted by Genetic Algorithms to establish the characteristic wavelength prediction models. Then, based on the original and preprocessed spectral data, full-band prediction models were established by using Partial Least Squares Regression and Gradient Boosting Regression Tree. Firstly, 12 algorithms were used to preprocess the original spectral data. The objective of this study was to predict the content of calcium, boron and potassium in the pulp and peel of ‘Huangguan’ pears nondestructively and conveniently by using near-infrared spectroscopy (900–1700 nm) technology. It has been proved that the imbalance of the proportion of elements of ‘Huangguan’ pears in the pulp and peel, especially calcium, boron and potassium, may be important factors that can seriously affect the pears’ appearance quality and economic benefits.
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