Classification of chili plant origin by using multilayer perceptron neural network

The geographical origin of the plants can affect the growth and hence the quality of the plants. In this research, the origin of the chili plants has been investigated by using Fourier transform infrared (FTIR) spectroscopy. The spectroscopy generated 3734 data with a wavenumber range from 4000–400 cm −1 . The pre-processing of the spectra was done by using baseline correction and vector normalization. The analysis was then taken in the biofingerprint area of 1800–900 cm −1 range which has 934 data points. Feature extraction for dimension reduction was achieved using principal component analysis (PCA). The PC scores from PCA were then fed into a k-means and a multilayer perceptron neural network (MLPNN). The k-means clustering shows that the samples can be distinguished into three different groups. Meanwhile, for the MLPNN, the number of the hidden layer's neurons and the learning rate of the system were optimized to get the best classification result. A hidden layer with twenty neurons had the highest accuracy, while a learning rate of 0.001 had the highest value of 100%

Gas chromatography-mass spectrometry analysis of compounds emitted by pepper yellow leaf curl virus-infected chili plants : a preliminary study

Pepper yellow leaf curl virus (PYLCV) is a threat to chili plants and can significantly reduce yields. This study aimed as a pilot project to detect PYLCV by analyzing compounds emitted by chili plants using gas chromatography-mass spectrometry (GC-MS). The samples investigated in this research were PYLCV-infected and PYLCV-undetected chili plants taken from commercial chili fields. The infection status was validated by using a polymerase chain reaction (PCR) test. A headspace technique was used to extract the volatile organic compounds emitted by plants. The analysis of GC-MS results began with pre-processing, analyzing sample compound variability with a boxplot analysis, and sample classification by using a multivariate technique. Unsupervised multivariate technique principal component analysis (PCA) was performed to discover whether GC-MS could identify PYLCV-infected or not. The results showed that PYLCV-infected and PYLCV-undetected chili plants could be differentiated, with a total percent variance of the first three principal components reaching 91.32%, and successfully discriminated between PYLCV-infected and PYLCV-undetected chili plants. However, more comprehensive studies are needed to find the potential biomarkers of the infected plants

Food security risk level assessment : a fuzzy logic-based approach

A fuzzy logic (FL)-based food security risk level assessment system is designed and is presented in this article. Three inputs—yield, production, and economic growth—are used to predict the level of risk associated with food supply. A number of previous studies have related food supply with risk assessment for particular types of food, but none of the work was specifically concerned with how the wider food chain might be affected. The system we describe here uses the Mamdani method. The resulting system can assess risk level against three grades: severe, acceptable, and good. The method is tested with UK (United Kingdom) cereal data for the period from 1988 to 2008. The approach is discussed on the basis that it could be used as a starting point in developing tools that may either assess current food security risk or predict periods or regions of impending pressure on food supply

Detection of potato storage disease via gas analysis : a pilot study using field asymmetric ion mobility spectrometry

Abstract: Soft rot is a commonly occurring potato tuber disease that each year causes substantial losses to the food industry. Here, we explore the possibility of early detection of the disease via gas/vapor analysis, in a laboratory environment, using a recent technology known as FAIMS (Field Asymmetric Ion Mobility Spectrometry). In this work, tubers were inoculated with a bacterium causing the infection, Pectobacterium carotovorum, and stored within set environmental conditions in order to manage disease progression. They were compared with controls stored in the same conditions. Three different inoculation time courses were employed in order to obtain diseased potatoes showing clear signs of advanced infection (for standard detection) and diseased potatoes with no apparent evidence of infection (for early detection). A total of 156 samples were processed by PCA (Principal Component Analysis) and k-means clustering. Results show a clear discrimination between controls and diseased potatoes for all experiments with no difference among observations from standard and early detection. Further analysis was carried out by means of a statistical model based on LDA (Linear Discriminant Analysis) that showed a high classification accuracy of 92.1% on the test set, obtained via a LOOCV (leave-one out cross-validation)

2-D Visualizations of the Frequency Contents of Lamb Waves in a Bovine Cortical Tibia

This study observes the frequency contents of Lamb waves propagating in a bone and represents them in 2-d graphics. A non-contact Lamb wave measurement technique, scanning laser vibrometry, is proposed to examine a bovine cortical tibia in vitro. The Lamb waves works at the center frequency of 84KHz. Only the fundamental modes, a(0) and s(0), were expected to occur. Defining the propagating Lamb wave modes is further performed using wavelet transform analysis

Optical parameters and space–bandwidth product optimization in digital holographic microscopy

This paper considers some of the most important optical parameters that characterize a digital holographic microscope (DHM) and presents their mathematical derivation based on geometrical and diffraction-based models. It supports and justifies the use of the out-of-focus recording of holograms by showing that the field of view can be increased when recording the hologram in front of the in-focus image plane. In this manner a better match between the space–bandwidth product (SBP) of the microscope objective and that of the reconstructed hologram can be obtained. Hence, DHM offers a more cost-efficient way to increase the recorded SBP compared to the application of a high-quality microscope objective (large numerical aperture and low magnification) used in conventional microscopy. Furthermore, an expression for the imaging distance (distance between hologram and image plane), while maintaining the optical resolution and sufficient sampling, is obtained. This expression takes into account all kinds of reference-wave curvature and can easily be transferred to lensless digital holography. In this context it could be demonstrated that an object wave matched reference wave offers a significantly smaller imaging distance and hence the largest recoverable SBP. In addition, a new, to our knowledge, approach, based on the influence of defocus on the modulation transfer function, is used to derive the depth of field (DOF) for a circular aperture (lens-based system) and a rectangular aperture (lensless system), respectively. This investigation leads to the finding that a rectangular aperture offers an increased resolution combined with an increased DOF, when compared to a circular aperture of the same size

Lamb waves detection in a bovine cortical tibia using scanning laser vibrometry

Most of the techniques for generating and detecting ultrasonic Lamb waves (e.g. angle-beam piezoelectric transducers, micro-electro mechanical systems (MEMS), comb and interdigital transducers, phased array transducers, and piezoceramic transducers) require a firm physical contact with the measured objects. For objects with highly irregular surfaces such as bones, it will be very difficult to produce a good contact. Thus, a non-contact Lamb wave measurement technique, the scanning laser vibrometry, is proposed in this paper to examine a bovine cortical tibia in vitro. The ultrasonic Lamb waves used had the center frequency of 84KHz. The waves were generated using a planar transducer which was coupled with a cone-shaped resonant vibrator. Only the fundamental modes of a(0) and s(0) were expected to occur. 2-Dimensional images of the Lamb waves traveling in the bone were recorded. The scan results represent out-of-plane vibration of the surface of the bone. Lamb wave modes were verified with further post-processing analyses. In time-domain, time-history prediction of the modes is fitted onto the original detected signal as to confirm their common rising time for each mode. A frequency-domain method, i.e. wavelet analysis, is also employed to define the traveling modes and their group velocity. The expected modes can be clearly defined at the center frequency. Additionally, what seemed to be a new mode, a,, was generated and detected at the higher frequency of the responses

Neural network based electronic nose for classification of tea aroma

This paper describes an investigation into the performance of a Neural Network (NN) based Electronic Nose (EN) system, which can discriminate the aroma of different tea grades. The EN system comprising of an array of four tin-oxide gas sensors was used to sniff thirteen randomly selected tea grades, which were exemplars of eight categories in terms of aroma profiles. The mean and peak of the transient signals generated by the gas sensors, as a result of aroma sniffing, were treated as the feature vectors for the analysis. Principal Component Analysis (PCA) was used to visualise the different categories of aroma profiles. In addition, K-means and Kohonen’s Self Organising Map (SOM) cluster analysis indicated there were eight clusters in the dataset. Data classification was performed using supervised NN classifiers; namely the Multi-Layer Perceptron (MLP) network, Radial Basis Function (RBF) network, and Constructive Probabilistic Neural Network (CPNN) were used for aroma classification. The results were that the three NNs performed as follows: 90.77, 92.31, and 93.85%, respectively in terms of classification accuracy. Hence the performance of the proposed method of aroma analysis demonstrates that it is possible to use NN based EN to assist with the tea quality monitoring procedure during the tea grading process. In addition the results indicate the possibility for standardization of the tea aroma in numeric terms

Principal component and factor analysis to study variations in the aging lumbar spine

Human spine is a multifunctional structure of human body consisting of bones, joints, ligaments and muscles which all undergo a process of change with the age. A sudden change in these features either naturally or thorough injury can lead to some serious medical conditions which puts huge burden on health services and economy. While aging is inevitable, the effect of aging on different areas of spine is of clinical significance. This paper reports the growth and degenerative pattern of human spine using principal component analysis. Some noticeable lumbar spine features such as vertebral heights, disc heights, disc signal intensities, para-spinal muscles, subcutaneous fats, psoas muscles and cerebrospinal fluid were used to study the variations seen on lumbar spine with the natural aging. These features were extracted from lumbar spine magnetic resonance images of 61 subjects with age ranging from 2 to 93 years. Principal component analysis is used to transform complex and multivariate feature space to a smaller meaningful representation. PCA transformation provided 2D visualization and knowledge of variations among spinal features. Further useful information about correlation among the spinal features is acquired through factor analysis. The knowledge of age related changes in spinal features are important in understanding different spine related problems