针对苹果内部缺陷在线检测的产业技术需求,研究基于透射光谱技术的苹果内部缺陷在线检测系统。研究设计了光源套件、专用光纤和果托式输送单元等关键部件,提升在线透射光谱质量、降低热损伤和机械损伤；解决了光电信号干扰问题,开发了专用检测软件,实现苹果内部品质信息的无损在线获取。比较分析了正常苹果与腐心病果的光谱响应差异,优化参数后设置在线检测速度3个/秒,触发控制光谱采集时间80 ms。在选择特征波长的基础上利用线性判别分析建立了苹果腐心病的在线判别模型,预测的总体识别率达90%以上。研究结果表明该系统可以实现苹果内部缺陷的快速、无损在线检测。

Internally defected apples are not easily distinguished from normal ones by their external appearances, since there are no visible defects on the exterior.Detection of internally defected apples with a suitable technique is thus crucial for quality control.Aimed to the nondestructive on line test of the internal defect of apple, this work presented the development of an on line detection prototype system using visible and near infrared(Vis/NIR) technology as a new approach for on line identifying the defects without sample destructiveness.The system included a fruit tray conveyor, an illumination source, a spectral acquisition unit, a photoelectric sensor, chassis, an industrial computer, a dark sample compartment, and an analysis unit.The critical components such as light source module, costumed fiber and transmission unit with separate tray were designed and developed to improve spectra signal quality, lower heat damage and reduce mechanical damage.The problem of photoelectric signal interference was solved by strong and weak electricity separation and metal shield.Special detection software was developed for real time inspection based on multithread programming technology.The advantages of this software were presented by the process of modular design, including software system initialization, information communication, information interaction, spectral data acquisition and processing, spectral curve real time display, defect category discrimination, statistics and saving of detection results.It is difficult to collect the internally defected apple samples from orchard, supermarket and wholesalers, because the symptoms are not externally recognizable and visible if the fruits are not cut.In this experiment, the apples with internal defects caused by core rot fungi were collected and cultivated.We tried the preparation of samples and achieved good performance.A total of 84 ‘Fuji’ apples were used to establish classification model, and another batch(a total of 71 samples) was on line measured for verification the robustness and applicability of model.The detection of internal quality information in nondestructive online way was achieved by this system.The differences of spectral response between intact and internally defected apple were compared and analyzed.Meanwhile, the varying degrees of defect apple were discussed.After the optimization of parameters, the conveyor was set at a speed of 3 apples within one second, and the integration time of the spectral collection was set to 80 ms.Spectral data were recorded as absorbance units.On the basis of selection characteristic wavelength, linear discriminant analysis (LDA) was implemented to establish a discriminant model of apple internal defects.The optimal LDA model was used to estimate the samples in the training set, and the total classification rate was 94.05% in the training set.The optimal LDA was used to test the new samples in the prediction set, and the total classification rate was 90.14% in the predication set.The classification results demonstrate that the LDA model has high and robust classification performance.Additionally, we could found that slight degree internal defect was difficult to identify, because it was small in the core of apple with weak spectra response.The proposed system could successfully differentiate the apple with internal defect from intact apple.The results showed that a nondestructive on line internal defect determination prototype based on Vis/NIR transmittance technique was feasible.In view of these results, the present research lays the foundation for the future development of an automatic system based on transmittance spectroscopy in the visible and NIR regions that is capable of detecting internal defects in apple fruits, which is extremely important from the economic point of view.The use of such detecting techniques potentially makes it possible to remove internally defected samples simply in a fast, nondestructive on line way for high quality control in fruit industries.

TP274.4；O433.4

国家自然科学基金(31501216)；国家科技支撑计划(2015BAD19B03)；江苏大学高级人才基金(15JDG169)