Aghilinategh Nahid, Dalvand Mohammad Jafar, Anvar Adieh
Department of Agricultural Machinery Engineering Sonqor Agriculture Faculty Razi University Kermanshah Iran.
Faculty of Agricultural Engineering and Technology University of Tehran Karaj Iran.
Food Sci Nutr. 2020 Jul 19;8(9):4919-4928. doi: 10.1002/fsn3.1788. eCollection 2020 Sep.
The estimation of ripeness is a significant section of quality determination since maturity at harvest can affect sensory and storage properties of fruits. A possible tactic for defining the grade of ripeness is sensing the aromatic volatiles released by fruit using electronic nose (e-nose). For detection of the five ripeness grades of berries (whiteberry and blackberry), the e-nose machine was designed and fabricated. Artificial neural networks (ANN), principal components analysis (PCA), and linear discriminant analysis (LDA) were applied for pattern recognition of array sensors. The best structure (10-11-5) can classify the samples in five classes in ANN analysis with a precision of 100% and 88.3% for blackberry and whiteberry, respectively. Also, PCA analysis characterized 97% and 93% variance in the blackberry and whiteberry, respectively. The least correct classification for whiteberry was observed in the LDA method.
成熟度的评估是质量判定的一个重要部分，因为收获时的成熟度会影响水果的感官和储存特性。一种确定成熟度等级的可行策略是使用电子鼻（e-nose）来感知水果释放的芳香挥发物。为了检测浆果（白莓和黑莓）的五个成熟度等级，设计并制造了电子鼻仪器。人工神经网络（ANN）、主成分分析（PCA）和线性判别分析（LDA）被用于阵列传感器的模式识别。最佳结构（10-11-5）在ANN分析中可以将样本分为五类，黑莓和白莓的分类精度分别为100%和88.3%。此外，PCA分析分别表征了黑莓和白莓中97%和93%的方差。在LDA方法中观察到白莓的正确分类最少。
