摘要
为了实现多果品内部品质的现场无损快速检测,基于多光谱技术设计了一种可更换探头的手持式多果品内部品质无损检测仪。该检测仪的硬件系统由主机和多光谱采集探头组成。主机包括微处理器、电源管理模块、稳压驱动模块及输入输出模块。多光谱采集探头由12个不同波长的发光二极管和数字光电传感器组成。在MDK 5.0开发环境下用C语言开发了检测仪的软件。利用开发的检测仪分别采集了“华优”猕猴桃和“雪”梨的漫反射多光谱,并基于偏最小二乘法建立了预测猕猴桃和梨的可溶性固形物含量(SSC)和硬度的模型。将预测模型导入到检测仪后对仪器的性能进行了测试。结果表明,该检测仪对猕猴桃SSC和硬度的测量均方根误差分别为1.51%和5.13 N,对梨SSC和硬度的测量均方根误差分别为0.52%和4.57 N,检测时间在2 s以内,可通过更换探头实现对多种果品内部品质的无损快速检测。
With the improvement of consumption level,the internal quality of fruit has become an important factor to attract consumers.However,the traditional methods used to measure soluble solids content(SSC) and firmness are destructive.To realize rapid non-destructive detection on the internal quality of multi-fruits,a handheld non-destructive detector for internal quality of multi-fruits with replaceable probe was developed.The hardware system of the detector consisted of a host and a multi-spectral acquisition probe.The host included a microprocessor,a power management module,a voltage regulator drive module and an input and output module.The multi-spectral acquisition probe included 12 light emitting diodes(LEDs) at different wavelengths and a digital optoelectronic sensor.The software system of the detector was developed in C language in the development environment at MDK 5.0.The diffuse reflectance multi-spectral of “Huayou” kiwifruit and “Xue” pear were collected by the developed detector,and the prediction models for SSC and firmness were established based on the partial-least-squares regression.After downloading the model into the detector,the detection performance of the detector was tested.The results showed that the root mean square errors of SSC and firmness prediction for kiwifruit were 1.51% and 5.13 N,and the root mean square errors of SSC and firmness prediction for pear were 0.52% and 4.57 N.Moreover,the measurement could be realized in 2 s.The measurement on the internal quality of multi-fruits was realized by replacing the probe of the detector.
作者
郭文川
纪同奎
张宗逸
周一航
GUO Wenchuan;JI Tongkui;ZHANG Zongyi;ZHOU Yihang(College of Mechanical and Electronic Engineering,Northwest A&F University,Yangling,Shaanxi 712100,China;Key Laboratory of Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs,Yangling,Shaanxi 712100,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2023年第2期403-409,共7页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金面上项目(31772065)
陕西省重点研发计划项目(2020ZDLNY03-06)。
关键词
猕猴桃
梨
内部品质
多光谱
检测仪
kiwifruit
pear
internal quality
multi⁃spectral
detector