1,721,026 research outputs found
紫外から近赤外領域の分光反射および蛍光特性に基づいたマシンビジョンによるジャガイモ表面の欠陥検出
Get PDF京都大学0048新制・課程博士博士(農学)甲第22075号農博第2367号新制||農||1072(附属図書館)学位論文||R1||N5229(農学部図書室)京都大学大学院農学研究科地域環境科学専攻(主査)教授 近藤 直, 准教授 小川 雄一, 教授 清水 浩学位規則第4条第1項該当Doctor of Agricultural ScienceKyoto UniversityDGA
Potato surface defect detection using machine vision systems based on spectral reflection and fluorescence characteristics in the UV-NIR region
No full textPotato Quality Grading Based on Depth Imaging and Convolutional Neural Network
No full textAs a cost-effective and nondestructive detection method, the machine vision technology has been widely applied in the detection of potato defects. Recently, the depth camera which supports range sensing has been used for potato surface defect detection, such as bumps and hollows. In this study, we developed a potato automatic grading system that uses a depth imaging system as a data collector and applies a machine learning system for potato quality grading. The depth imaging system collects 3D potato surface thickness distribution data and stores depth images for the training and validation of the machine learning system. The machine learning system, which is composed of a softmax regression model and a convolutional neural network model, can grade a potato tube into six different quality levels based on tube appearance and size. The experimental results indicate that the softmax regression model has a high accuracy in sample size detection, with a 94.4% success rate, but a low success rate in appearance classification (only 14.5% for the lowest group). The convolutional neural network model, however, achieved a high success rate not only in size classification, at 94.5%, but also in appearance classification, at 91.6%, and the overall quality grading accuracy was 86.6%. The quality grading based on the depth imaging technology shows its potential and advantages in nondestructive postharvesting research, especially for 3D surface shape-related fields
Studi Pengaruh Metode Pengeringan Vakum terhadap Karakteristik Fisik Bunga Mawar (Rosa hybrida) Kering
No full textBunga mawar (Rosa hybrida) merupakan tanaman hias yang paling populer di kalangan masyarakat. Tanaman mawar sebagai tanaman hias memiliki nilai jual yang tinggi. Namun, bunga mawar mudah mengalami kelayuan dalam beberapa hari salah satunya akibat keberadaan hormon etilen. Proses pengawetan bunga hias perlu dilakukan salah satunya melalui proses pengeringan untuk mempertahankan kondisi fisik pada bunga mawar dan dapat mempertahankan umur simpan serta menjaga kualitas pada bunga. Penelitian ini bertujuan untuk menganalisis perubahan kadar air, laju pengeringan, warna, penyusutan, dan kinetika pemodelan pengeringan serta mengevaluasi perbandingan efektivitas pengering vakum pada pengeringan bunga mawar. Metode pengeringan vakum dipilih pada penelitian ini karena memiliki tekanan di bawah atmosfer yang memungkinkan terjadinya penurunan titik didih air dan dapat berlangsung pada suhu rendah sehingga proses pengeringan lebih cepat dan tidak merusak tampak visual pada bunga. Penambahan silika gel pada penelitian ini dapat memaksimalkan proses dan hasil pengeringan. Pengeringan dilakukan pada suhu 50°C dengan tekanan -68 cmHg dengan variasi metode pengering vakum, pengering udara panas, dan pengering konvensional. Pengujian yang dilakukan berupa kadar air, laju pengeringan, penyusutan, dan warna. Penelitian ini dilakukan dengan rancangan acak lengkap faktorial serta metode analisis Two Way ANOVA dan uji lanjut Duncan. Hasil penelitian menunjukkan bahwa penggunaan metode pengeringan vakum menghasilkan rata-rata kadar air terendah mencapai 6,68% dalam waktu 14 jam dengan rata-rata laju pengeringan sebesar 0,24%. Hasil pengujian warna berpengaruh sangat signifikan terhadap tingkat kecerahan (L*) dan tingkat kemerahan (a*), serta tidak berpengaruh signifikan terhadap tingkat kekuningan (b*). Sedangkan hasil penyusutan tidak berpengaruh signifikan terhadap penggunaan metode dan perlakuan
Classification of raw Ethiopian honeys using front face fluorescence spectra with multivariate analysis
No full textNon-Targeted Detection and Quantification of Food Adulteration of High-Quality Stingless Bee Honey (SBH) via a Portable LED-Based Fluorescence Spectroscopy
No full textStingless bee honey (SBH) is rich in phenolic compounds and available in limited quantities. Authentication of SBH is important to protect SBH from adulteration and retain the reputation and sustainability of SBH production. In this research, we use portable LED-based fluorescence spectroscopy to generate and measure the fluorescence intensity of pure SBH and adulterated samples. The spectrometer is equipped with four UV-LED lamps (peaking at 365 nm) as an excitation source. Heterotrigona itama, a popular SBH, was used as a sample. 100 samples of pure SBH and 240 samples of adulterated SBH (levels of adulteration ranging from 10 to 60%) were prepared. Fluorescence spectral acquisition was measured for both the pure and adulterated SBH samples. Principal component analysis (PCA) demonstrated that a clear separation between the pure and adulterated SBH samples could be established from the first two principal components (PCs). A supervised classification based on soft independent modeling of class analogy (SIMCA) achieved an excellent classification result with 100% accuracy, sensitivity, specificity, and precision. Principal component regression (PCR) was superior to partial least squares regression (PLSR) and multiple linear regression (MLR) methods, with a coefficient of determination in prediction (R2p) = 0.9627, root mean squared error of prediction (RMSEP) = 4.1579%, ratio prediction to deviation (RPD) = 5.36, and range error ratio (RER) = 14.81. The LOD and LOQ obtained were higher compared to several previous studies. However, most predicted samples were very close to the regression line, which indicates that the developed PLSR, PCR, and MLR models could be used to detect HFCS adulteration of pure SBH samples. These results showed the proposed portable LED-based fluorescence spectroscopy has a high potential to detect and quantify food adulteration in SBH, with the additional advantages of being an accurate, affordable, and fast measurement with minimum sample preparation
A preliminary study on the potential of front face fluorescence spectroscopy for Italian mono-cultivar extra virgin olive oil discrimination
Get PDFFront-face fluorescence method has been used to obtain Fluorescence Excitation Emission Matrix (EEM) characteristic of various Italian monocultivar Extra Virgin Olive Oil (EVOO), Mixed EVOO, and lower grade Virgin Olive Oil (VOO). Specific region of EEM has been explored using Principle Component Analysis (PCA). It was found that EEM region of excitation 250-400 and emission 280-620 nm could be used to discriminate each sample type of different cultivar, mixed sample, and virgin olive oil sample. The important fluorescence peaks for the discrimination belong to unique combination of Tocopherols, Tocotrienols, Phenolic compounds, Oxidation products, and Vitamin E. These results show that front-face Fluorescence EEM spectroscopy has a potential to be used for monocultivar EVOO discrimination
Diffuse reflectance characteristic of potato surface for external defects discrimination
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