不同成熟期哈密瓜坚实度变化规律及相关性研究

Study on variation and correlation of firmness index of Hami melon at different maturity stages

摘要

坚实度是哈密瓜成熟度的重要参考指标之一。为了研究哈密瓜成熟期坚实度的变化规律，结合试验条件，对2种不同成熟期的哈密瓜质量、纵横径、坚实度等指标进行测量，研究不同品种、不同成熟期、不同原始光谱、不同检测部位哈密瓜的坚实度指标变化规律，分析各指标之间的相关性，并结合高光谱技术建立哈密瓜不同检测部位坚实度与成熟度的预测模型。试验建模结果显示，赤道阳面部位的模型相关系数:校正集相关系数 Rc=0.982，预测集相关系数 Rp=0.832；赤道阴面部位的模型相关系数: Rc=0.889， Rp=0.878；果脐部位的模型相关系数: Rc=0.626， Rp=0.821。结果表明，哈密瓜坚实度指标的变化规律与成熟度具有相关性，且不同检测部位的坚实度值存在差异，赤道部位检测结果优于果脐部位。此研究结果为确定哈密瓜的最佳采摘期提供理论参考依据。

Abstract

Firmness is one of the important reference indexes of Hami melon maturity. In order to study the change rule of firmness of Hami melon at maturity stage, this paper combined with experimental conditions to measure the weight, longitudinal and transverse diameter, firmness and other physical and chemical indexes of two kinds of Hami melon at different maturity stages. The firmness index of Hami melon with different varieties, different maturity stages, different original spectra and different test sites were studied, and the correlation between the indexes was analyzed. The prediction models of firmness and maturity of different parts of Hami melon were established by hyperspectral technique. The experimental modeling results showed that the correlation coefficients of the equatorial sunny side were Rc=0.982, Rp=0.832. The model correlation coefficient of the dark side of the equator: Rc=0.889, Rp=0.878. And the model correlation coefficient of fruit umbilical position: Rc=0.626, Rp=0.821. The results showed that the firmness index of Hami melon was correlated with the maturity, and the firmness of different parts was different. The results provide a theoretical basis for determining the optimum picking time of Hami melon.

关键词

哈密瓜 / 成熟期 / 坚实度 / 变化规律 / 模型

Key words

Hami melon / Maturity stage / Firmness / The law of change / Model

参考文献

[1] 许德芳. 基于深度学习和近红外技术的甜瓜采摘检测及货架期研究[D]. 太原: 山西农业大学, 2020.

[2] 广新梅, 徐崇志, 王绪春, 等. 采摘期内库尔勒香梨品质指标变化规律及相关关系[J]. 江苏农业科学, 2018, 46(19): 118-121.

[3] 兰海鹏. 库尔勒香梨成熟特征表达及评价方法[D]. 哈尔滨: 东北农业大学, 2017.

[4] 李艳婷, 周铮, 黄冬华, 等. 翠冠梨果不同部位可溶性固形物及硬度测定比较研究[J]. 中国南方果树, 2022, 51(3): 148-152.

[5] 刘金秀, 贺小伟, 罗华平, 等. 不同成熟期小白杏品质指标变化规律研究[J]. 塔里木大学学报, 2021, 33(1): 38-44.

[6] 牛俊莉, 刘茂秀, 彭秋梅. 不同采摘期杏李味厚品种理化指标主成分及聚类分析[J]. 新疆农业科学, 2015, 52(1): 33-36.

[7] 代佳慧, 尹青春, 党政, 等. 不同成熟度胡椒理化指标变化规律和相关性研究[J]. 中国调味品, 2020, 45(7): 81-85.

[8] 贺春祥, 王辉, 谭江, 等. 延安十二个苹果品种果实主要理化指标分析评价[J]. 西北园艺(果树), 2022(4): 50-52.

[9] SHINYA P, CONTADOR L, PREDIERI S, et al. Peach ripening: Segregation at harvest and postharvest flesh softening[J]. Postharvest Biology and Technology, 2013, 86: 472-478.

[10] ZHANG C, GUO C T, LIU F, et al. Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine[J]. Journal of Food Engineering, 2016, 179: 11-18.

[11] GAO Z M, SHAO Y Y, XUAN G T, et al. Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning[J]. Artificial Intelligence in Agriculture, 2020, 4: 31-38.

[12] 张巍. 基于高光谱成像技术的蓝莓内部品质检测方法的研究[D]. 沈阳: 沈阳农业大学, 2016.

[13] 卢娜, 韩平, 王纪华. 基于高光谱成像技术的草莓硬度预测[J]. 软件导刊, 2018, 17(3): 180-182.

[14] REDDY R R, LI M. Uncertainty assessment for firmness and total soluble solids of sweet cherries using hyperspectral imaging and multivariate statistics[J]. Journal of Food Engineering, 2021, 289: 110177.

[15] XIE C Q, CHU B Q, HE Y. Prediction of banana color and firmness using a novel wavelengths selection method of hyperspectral imaging[J]. Food Chemistry, 2018, 245: 132-140.

[16] SHEN L X, WANG H H, LIU Y, et al. Prediction of soluble solids content in green plum by using a sparse autoencoder[J]. Applied Sciences, 2020, 10(11): 3769.

[17] 侯宝路, 李柏承, 周瑶, 等. 基于可见-近红外高光谱成像技术的梨硬度和可溶性固体含量的预测[J]. 激光杂志, 2015, 36(10): 70-74.

[18] ZHANG H L, ZHAN B S, PAN F, et al. Determination of soluble solids content in oranges using visible and near infrared full transmittance hyperspectral imaging with comparative analysis of models[J]. Postharvest Biology and Technology, 2020, 163(3): 111148.

[19] 何洪巨, 胡丽萍, 李武, 等. 基于可见/近红外高光谱成像技术的西甜瓜糖度检测[J]. 中国食物与营养, 2016, 22(10): 53-57.

[20] SUN M J, ZHANG D, LIU L, et al. How to predict the sugariness and hardness of melons: A near-infrared hyperspectral imaging method[J]. Food Chemistry, 2017, 218: 413-421.

[21] 刘超. 基于可见-近红外光谱与机器视觉信息融合的河套蜜瓜糖度检测方法研究[D]. 呼和浩特: 内蒙古农业大学, 2014.

[22] 许济海. 基于纹理特征的网纹哈密瓜分类研究[D]. 杭州: 浙江大学, 2017.

[23] 吕琛. 基于数字图像处理的哈密瓜成熟度无损检测技术研究[D]. 新疆石河子: 石河子大学, 2014.

[24] 胡光辉, 郭俊先, 虞飞宇, 等. “京蜜八号”哈密瓜成熟等级分类研究:基于机器视觉和神经网络[J]. 农机化研究, 2015, 37(2): 190-194.

[25] 李明周. 基于高光谱哈密瓜生长过程品质的检测研究[D]. 新疆阿拉尔: 塔里木大学, 2018.

[26] 徐璐. 基于可见-近红外光谱及成像技术的水果可溶性固形物含量检测[D]. 合肥: 安徽大学, 2019.

基金资助

新疆维吾尔自治区教育厅高校基本科研业务费项目(XJEDU2022P128)

兵团十二师科技计划项目(SR2022003)