Terahertz sensing analysis for early detection of ganoderma boninense disease using near infrared (NIR) spectrometer

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a significant impact on the economic viability of oil palm plantations. Early detection is critical for the effective management of this disease since there is no effective treatment that can stop the spread of this disease. The proposed system uses integrated hand-held near-infrared spectroscopy (NIRS) for early detection of G. boninense on asymptomatic oil palm seedlings and classification of spectral data using machine learning (ML) techniques. The non-destructive method using NIRS with ML and predictive analytics has the potential to be a highly sensitive and reliable method for the early detection of G. boninense. Spectral data are collected from 6 samples of inoculated and non-inoculated oil palm samples at nursery stages using an integrated NIRS sensor. Chemometrics is performed by implementing principal component analysis (PCA), derivatives and partial least square (PLS) regression to extract the vital information of the spectra. The significant wavelengths are at 1310 nm and 1450 nm which are attributable to ergosterol and water content, respectively. Furthermore, the SG derivatives spectra peaks corresponded to specific functional groups that could be utilized for the detection of G. boninense. These functional groups encompass the third overtone of N-H stretching, the second overtone of C-H stretching, and a combination band involving both C-H stretching and O-H stretching. High-performance liquid chromatography (HPLC) analysis is performed to identify the ergosterol content in oil palm sample. Ergosterol can be used as a biomarker for the detection of G. boninense since it can only be found in the fungal-infested plant. In classification, four different ML algorithms: K-Nearest Neighbour (kNN), Naïve Bayes (NB), Support Vector Machine (SVM) and Decision Tree (DT) are tested to classify healthy and infected oil palm samples. DT algorithm on leaves spectra achieves a satisfactory overall performance compared to the other classifiers with high accuracy up to 93.1% and an F1-score of 92.6%. Therefore, a DT-based predictive analytic on leaves NIR spectral reference data is developed for real-time detection of G. boninense infection. A portable smart G. boninense detection system prototype is developed by implementing the Internet of Things (IoT) into the system which enables the integration of sensors and server to perform prediction of healthy or infected oil palm seedlings. This working prototype showed that this proposed approach is reliable and practical for the early detection of G. boninense in oil palm seedlings

VIS-NIR spectral and particles distribution of Au, Ag, Cu, Al and Ni nanoparticles synthesized in distilled water using laser ablation

In this research, gold (Au), silver (Ag), copper (Cu), aluminium (Al) and nickel (Ni) nanoparticles have been prepared by laser ablation in distilled water using Q-switched Nd: YAG. Comparative analysis between 1064 nm and 532 nm laser wavelength in term of absorption spectra and particle size distribution is performed. The synthesized nanoparticles were characterized by visible (VIS) and near infrared (NIR) spectrometer and transmission electron microscopy (TEM). The effect of laser pulse energy and laser wavelength on the size distribution and absorbance spectrum of metal nanoparticles was studied. The absorption peak intensities of the nanoparticles increase at higher laser pulse energy. It is discovered that for all metals, 532 nm laser produced nanoparticles with higher absorption peak intensities than 1064 nm. The work also shows that there is no noticeable difference in the size of the nanoparticles produced at 532 and 1064 nm for the Ni and Cu metals. However, a considerable size difference can be seen for Au, Ag, and Al at the two laser wavelengths. Smaller Ag nanoparticles were produced by 1064 nm laser while smaller Au and Al nanoparticles were produced by 532 nm laser

Ganoderma boninense classification based on near-infrared spectral data using machine learning techniques

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a severe threat to the palm oil industry. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease unless ergosterol, a biomarker of G. boninense can be detected. There is yet a non-destructive and in-situ technique explored to detect ergosterol. Capability of NIR to detect few biomarkers such as mycotoxin and zearalenone (ZEN) has been proven to pave the way an effort to explore NIR’s sensitivity towards detecting ergosterol, as discussed in this paper. A compact hand-held NIR with a measurement range of 900–1700 nm is utilized by scanning the leaves of three oil palm seedlings inoculated with G. boninense while the other three were non-inoculated from 16-weeks-old to 32-weeks-old. Significant changes of spectral reflectance have been notified occur at the wavelength of ~1450 nm which reflectance of infected sample is higher 0.2–0.4 than healthy sample which 0.1–0.19. The diminishing of the spectral curve at approximately 1450 nm is strongly suspected to happened due to the loss of water content from the leaves since G. boninense attacks the roots and causes the disruption of water supply to the other part of plant. However, a few overlapped NIRs’ spectral data between healthy and infected samples require for further validation which chemometric and machine learning (ML) classification technique are chosen. It is found the spectra of healthy samples are scattered on the negative sides of PC-1 while infected samples tend to be on a positive side with large loading coefficients marked significant discriminatory effect on healthy and infected samples at the wavelength of 1310 and 1452 nm. A PLS regression is used on NIR spectra to implement the prediction of ergosterol concentration which shows good corelation of R = 0.861 between the ergosterol concentration and oil palm NIR spectra. Four different ML algorithms are tested for prediction of G. boninense infection: K-Nearest Neighbour (kNN), Naïve Bayes (NB), Support Vector Machine (SVM) and Decision Tree (DT) are tested which depicted DT algorithm achieves a satisfactory overall performance with high accuracy up to 93.1% and F1-score of 92.6% compared to other algorithms. High accuracy shows the capability of the classification model to correctly predict the G. boninense detection while high F1-score indicates that the classification is able to validate the detection of G. boninense correctly with low misclassification rate. The result represents a significant step in the development of a nondestructive and in-situ detection system which validated by both chemometric and machine learning (ML) classification technique

Correlation of Near-Infrared (Nir) Spectroscopy with Water Quality Sensors to Detect Concentration of Saccharomyces Boulardii in Water

Terahertz (THz) sensing of high sensitivity detection has given the possibility of a non-invasive method for measuring and monitoring microorganism from water resources. The purpose of this study is to analyse the effectiveness of Near-infrared (NIR) spectroscopy as a non-destructive and in-situ measurement-based method for detection of Saccharomyces boulardii (S. boulardii) in water. Samplings are prepared in biotechnology lab in Universiti Malaysia Pahang (UMP), a yeast species of S. boulardii is used as a model microorganism. A single colony of yeast was inoculated in liquid broth media and incubated for overnight culture. A standard serial dilution method was applied to prepare five samples at different yeast concentration in corresponding test tubes of 0%, 10%, 20%, 50% and 100%. A hand-held NIR spectroscopy with range from 900nm to 1700nm wavelength is deployed gapless to scan those test tubes through its optical window. Meanwhile another sample with similar concentrations are inoculated into volume 0.0071 m3 of water equipped with water quality sensor system for monitoring and analysis purpose. The findings show inoculation certain concentration of 10%, 20%, 50% and 100% of S. boulardii into the water generated certain level of NIR spectroscopy’s spectral absorbance of 0.723, 0.64, 0.357 and 0.121 correspondingly at 1067 nm wavelength. This proves NIR spectroscopy is a highly-sensitivity THz sensor at 1067 nm wavelength as absorbance is at the minimum level as S. boulardii concentration is at the maximum. This finding is further validated by Dissolved Oxygen (DO) sensor which demonstrates rising maximum of 8 ppm after an hour of S. boulardii’s inoculation compared to 4 ppm in a normal water. However, the DO level back to normal after 5 hours due to the acclimatization process of the yeast and demonstrate capability of DO sensor to detect presence of yeast in water. PCA and PLS analysis based NIR spectroscopy’s spectral absorbance also demonstrates ability to categorise severity of a microbial illness depending on its concentration. The results from this study has suggested that the NIR spectroscopy sensor as an excellent option for microbial sensing in water

Ganoderma boninense disease detection by near-infrared spectroscopy classification: a review

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future

Ganoderma boninense disease detection by Near-Infrared Spectroscopy Classification: a review

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future

Near-infrared spectroscopy for ganoderma boninense detection in oil palm: An outlook

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causing a serious threat to the palm oil industry. This catastrophic disease ultimately destructs the basal tissues of oil palm that causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This mini-review describes past and future prospects of integrated research of near infrared spectroscopy (NIRS) towards early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and hyperspectral in reacting with organic tissues, ii) NIR spectrum is more precise and sensitive to particular diseases include G. boninense compared to visible light iii) hand-held NIRS for in-situ measurement is to explore the efficacy for early detection system in real-time using machine learning (ML) classifier algorithms and predictive analytics model. This non-destructive, environmentally friendly (no chemical involved), mobile and sensitive leads the integrated hand-held NIRS with ML, and predictive analytics has significant potential as a platform towards early detection of G. boninense in the future