Phenotyping Austrian Pine for Resistance Using Fourier-Transform Infrared Spectroscopy

2020 ◽  
Vol 46 (4) ◽  
pp. 276-286
Author(s):  
Anna Conrad ◽  
Caterina Villari ◽  
Patrick Sherwood ◽  
Pierluigi (Enrico) Bonello
Keyword(s):  
Fourier Transform ◽  
Urban Landscape ◽  
Economic Value ◽  
Fourier Transform Infrared ◽  
Pinus Nigra ◽  
Support Vector ◽  
Least Squares Regression ◽  
Midwestern Usa ◽  
Ft Ir ◽  
Plant Pathosystems

Austrian pine (Pinus nigra) is a valuable component of the urban landscape in the Midwestern USA. In this area, it is impacted by the fungal pathogen Diplodia sapinea, which causes a tip blight and canker on infected trees. While the disease can be managed through the application of fungicides and/or by preventing environmental conditions that are favorable for the pathogen, these practices only temporarily alleviate the problem. A more sustainable solution is to use resistant trees. The objective of this study was to evaluate whether Fourier-transform infrared (FT-IR) spectroscopy combined with chemometric analysis can distinguish between trees that vary in susceptibility to D. sapinea. Trees were phenotyped for resistance to D. sapinea by artificially inoculating shoots and measuring ensuing lesions seven days following inoculation. Then, three different chemometric approaches, including a type of machine learning called support vector machine (SVM), were used to evaluate whether or not trees that varied in susceptibility could be distinguished. Trees that varied in susceptibility could be discriminated based on FT-IR spectra collected prior to pathogen infection using the three chemometric approaches: soft independent modeling of class analogy, partial least squares regression, and SVM. While further validation of the predictive models is needed, the results suggest that the approach may be useful as a tool for screening and breeding Austrian pine for resistance to D. sapinea. Furthermore, this approach may have wide applicability in other tree/plant pathosystems of concern and economic value to the nursery and ornamental industries.

scholarly journals Rapid and Quantitative Detection of the Microbial Spoilage of Meat by Fourier Transform Infrared Spectroscopy and Machine Learning

2002 ◽  
Vol 68 (6) ◽  
pp. 2822-2828 ◽  
Author(s):  
David I. Ellis ◽  
David Broadhurst ◽  
Douglas B. Kell ◽  
Jem J. Rowland ◽  
Royston Goodacre
Keyword(s):  
Fourier Transform ◽  
Control Point ◽  
Fourier Transform Infrared ◽  
Sample Surface ◽  
Quantitative Detection ◽  
Least Squares Regression ◽  
Noninvasive Technique ◽  
Microbial Spoilage ◽  
Critical Control Point ◽  
Ft Ir

ABSTRACT Fourier transform infrared (FT-IR) spectroscopy is a rapid, noninvasive technique with considerable potential for application in the food and related industries. We show here that this technique can be used directly on the surface of food to produce biochemically interpretable “fingerprints.” Spoilage in meat is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. FT-IR was exploited to measure biochemical changes within the meat substrate, enhancing and accelerating the detection of microbial spoilage. Chicken breasts were purchased from a national retailer, comminuted for 10 s, and left to spoil at room temperature for 24 h. Every hour, FT-IR measurements were taken directly from the meat surface using attenuated total reflectance, and the total viable counts were obtained by classical plating methods. Quantitative interpretation of FT-IR spectra was possible using partial least-squares regression and allowed accurate estimates of bacterial loads to be calculated directly from the meat surface in 60 s. Genetic programming was used to derive rules showing that at levels of 107 bacteria·g−1 the main biochemical indicator of spoilage was the onset of proteolysis. Thus, using FT-IR we were able to acquire a metabolic snapshot and quantify, noninvasively, the microbial loads of food samples accurately and rapidly in 60 s, directly from the sample surface. We believe this approach will aid in the Hazard Analysis Critical Control Point process for the assessment of the microbiological safety of food at the production, processing, manufacturing, packaging, and storage levels.

scholarly journals Detection of Gastric Cancer with Fourier Transform Infrared Spectroscopy and Support Vector Machine Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
Qingbo Li ◽  
Wei Wang ◽  
Xiaofeng Ling ◽  
Jin Guang Wu
Keyword(s):  
Support Vector Machine ◽  
Fourier Transform ◽  
Fourier Transform Infrared ◽  
Support Vector ◽  
Tissue Samples ◽  
Normal Tissues ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Ft Ir ◽  
Leave One Out

Early diagnosis and early medical treatments are the keys to save the patients' lives and improve the living quality. Fourier transform infrared (FT-IR) spectroscopy can distinguish malignant from normal tissues at the molecular level. In this paper, programs were made with pattern recognition method to classify unknown samples. Spectral data were pretreated by using smoothing and standard normal variate (SNV) methods. Leave-one-out cross validation was used to evaluate the discrimination result of support vector machine (SVM) method. A total of 54 gastric tissue samples were employed in this study, including 24 cases of normal tissue samples and 30 cases of cancerous tissue samples. The discrimination results of SVM method showed the sensitivity with 100%, specificity with 83.3%, and total discrimination accuracy with 92.2%.

scholarly journals Exploratory Study on Screening Chronic Renal Failure Based on Fourier Transform Infrared Spectroscopy and a Support Vector Machine Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Yushuai Yuan ◽  
Li Yang ◽  
Rui Gao ◽  
Cheng Chen ◽  
Min Li ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Renal Failure ◽  
Fourier Transform ◽  
Chronic Renal Failure ◽  
Ir Spectroscopy ◽  
Fourier Transform Infrared ◽  
Diagnostic Methods ◽  
Support Vector ◽  
Ft Ir ◽  
Ft Ir Spectroscopy

Chronic renal failure (CRF) is a clinically serious kidney disease. If the patient is not treated in a timely manner, CRF will develop into uremia. However, current diagnostic methods, such as routine blood examinations and medical imaging, have low sensitivity. Therefore, it is important to explore new and effective diagnostic methods for CRF, such as serum spectroscopy. This study proposes a cost-effective and reliable method for detecting CRF based on Fourier transform infrared (FT-IR) spectroscopy and a support vector machine (SVM) algorithm. We measured and analyzed the FT-IR spectra of serum from 44 patients with CRF and 54 individuals with normal renal function. The partial least squares (PLS) algorithm was applied to reduce the dimensionality of the high-dimensional spectral data. The samples were input into the SVM after division by the Kennard–Stone (KS) algorithm. Compared with other models, the SVM optimized by a grid search (GS) algorithm performed the best. The sensitivity of our diagnostic model was 93.75%, the specificity was 100%, and the accuracy was 96.97%. The results demonstrate that FT-IR spectroscopy combined with a pattern recognition algorithm has great potential in screening patients with CRF.

scholarly journals Application of Fourier Transform Infrared Spectroscopy and Multivariate Analysis Methods for the Non-Destructive Evaluation of Phenolics Compounds in Moringa Powder

Agriculture ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 10
Author(s):  
Rahul Joshi ◽  
Ramaraj Sathasivam ◽  
Sang Un Park ◽  
Hongseok Lee ◽  
Moon S. Kim ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Phenolic Compounds ◽  
Ir Spectroscopy ◽  
High Performance ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Least Squares Regression ◽  
Ft Ir ◽  
Non Destructive ◽  
Ft Ir Spectroscopy

This study performed non-destructive measurements of phenolic compounds in moringa powder using Fourier Transform Infrared (FT-IR) spectroscopy within a spectral range of 3500–700 cm−1. Three major phenolic compounds, namely, kaempferol, benzoic acid, and rutin, were measured in five different varieties of moringa powder, which was approved with respect to the high-performance liquid chromatography (HPLC) method. The prediction performance of three different regression methods, i.e., partial least squares regression (PLSR), principal component regression (PCR), and net analyte signal (NAS)-based methodology, called hybrid linear analysis (HLA/GO), were compared to achieve the best prediction model. The obtained results for the PLS regression method resulted in better performance for the prediction analysis of phenolic compounds in moringa powder. The PLSR model attained a correlation coefficient () value of 0.997 and root mean square error of prediction (RMSEP) of 0.035 mg/g, respectively, which is comparatively higher than the other two regression models. Based on the results, it can be concluded that FT-IR spectroscopy in conjugation with a suitable regression analysis method could be an effective analytical tool for the non-destructive prediction of phenolic compounds in moringa powder.

Detection Limits for Blood on Fabrics Using Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy and Derivative Processing

2016 ◽  
Vol 71 (5) ◽  
pp. 839-846 ◽  
Author(s):  
Zhenyu Lu ◽  
Stephanie A. DeJong ◽  
Brianna M. Cassidy ◽  
Raymond G. Belliveau ◽  
Michael L. Myrick ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Ir Spectra ◽  
Front Surface ◽  
Fourier Transform Infrared ◽  
Total Reflection ◽  
Attenuated Total Reflection ◽  
Least Squares Regression ◽  
Blood Concentrations ◽  
Ft Ir ◽  
Preprocessing Technique

Attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) was used to detect blood stains based on signature protein absorption in the mid-IR region, where intensity changes in the spectrum can be related to blood concentration. Partial least squares regression (PLSR) was applied for multivariate calibrations of IR spectra of blood dilutions on four types of fabric (acrylic, nylon, polyester, and cotton). Gap derivatives (GDs) were applied as a preprocessing technique to optimize the performance of calibration models. We report a much improved IR detection limit (DL) for blood on cotton (2700× in dilution factor units) and the first IR DL reported for blood on nylon (250×). Due to sample heterogeneity caused by fabric hydrophobicity, acrylic fabric produced variable ATR FT-IR spectra that caused poor DLs in concentration units compared to previous work. Polyester showed a similar problem at low blood concentrations that lead to a relatively poor DL as well. However, the increased surface sensitivity and decreased penetration depth of ATR FT-IR make it an excellent choice for detection of small quantities of blood on the front surface of all fabrics tested (0.0010 µg for cotton, 0.0077 µg for nylon, 0.011 µg for acrylic, and 0.0066 µg for polyester).

Determination of ß-Carotene and Lutein in Green Tea Using Fourier Transform Infrared Spectroscopy

2019 ◽  
Vol 62 (1) ◽  
pp. 75-81 ◽  
Author(s):  
Yong He ◽  
Yong He ◽  
Yiying Zhao ◽  
Chu Zhang ◽  
Chanjun Sun ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Infrared Spectroscopy ◽  
Ir Spectroscopy ◽  
Least Squares ◽  
Fourier Transform Infrared Spectroscopy ◽  
Green Tea ◽  
Fourier Transform Infrared ◽  
Support Vector ◽  
Ft Ir ◽  
Ft Ir Spectroscopy

Abstract. The feasibility of using Fourier transform infrared (FT-IR) spectroscopy combined with chemometrics to determine the ß-carotene and lutein contents in green tea was investigated in this study. The relationship between pigment contents and spectral responses was explored by partial least squares (PLS), least squares support vector machine (LS-SVM), and extreme learning machine (ELM) methods. Next, 30 and 29 effective wavenumbers (EWs) for ß-carotene and lutein, respectively, were selected according to the weighted regression coefficients of the PLS regression models, and simplified determinant models were built on the extracted EWs. The ELM models based on the EWs obtained the best results, with correlation coefficients of calibration (rc) and prediction (rp), and residual prediction deviation (RPD) of 0.977, 0.946, and 2.84, respectively, for ß-carotene and 0.975, 0.937, and 2.88, respectively, for lutein. The overall results indicate that FT-IR spectroscopy combined with chemometrics could be a rapid and accurate alternative method for determining carotenoid pigments in green tea. Keywords: ß-carotene, Chemometrics, Fourier transform infrared spectroscopy, Green tea, Lutein.

FR-IR Molecular Microanalysis System

1990 ◽  
Vol 48 (2) ◽  
pp. 270-271
Author(s):  
John A. Reffner ◽  
William T. Wihlborg
Keyword(s):  
Fourier Transform ◽  
Fourier Transform Infrared ◽  
Integrated System ◽  
Morphological Examination ◽  
Fully Integrated ◽  
Ir Microscopy ◽  
Normal Functions ◽  
Infrared Microspectroscopy ◽  
Infrared Spectral ◽  
Ft Ir

The IRμs™ is the first fully integrated system for Fourier transform infrared (FT-IR) microscopy. FT-IR microscopy combines light microscopy for morphological examination with infrared spectroscopy for chemical identification of microscopic samples or domains. Because the IRμs system is a new tool for molecular microanalysis, its optical, mechanical and system design are described to illustrate the state of development of molecular microanalysis. Applications of infrared microspectroscopy are reviewed by Messerschmidt and Harthcock.Infrared spectral analysis of microscopic samples is not a new idea, it dates back to 1949, with the first commercial instrument being offered by Perkin-Elmer Co. Inc. in 1953. These early efforts showed promise but failed the test of practically. It was not until the advances in computer science were applied did infrared microspectroscopy emerge as a useful technique. Microscopes designed as accessories for Fourier transform infrared spectrometers have been commercially available since 1983. These accessory microscopes provide the best means for analytical spectroscopists to analyze microscopic samples, while not interfering with the FT-IR spectrometer’s normal functions.

scholarly journals Variety Identification of Orchids Using Fourier Transform Infrared Spectroscopy Combined with Stacked Sparse Auto-Encoder

Molecules ◽  
2019 ◽  
Vol 24 (13) ◽  
pp. 2506 ◽  
Author(s):  
Yunfeng Chen ◽  
Yue Chen ◽  
Xuping Feng ◽  
Xufeng Yang ◽  
Jinnuo Zhang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Spectroscopic Technique ◽  
Variety Identification ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Discriminant Models ◽  
The Fourier Transform ◽  
Better Than

The feasibility of using the fourier transform infrared (FTIR) spectroscopic technique with a stacked sparse auto-encoder (SSAE) to identify orchid varieties was studied. Spectral data of 13 orchids varieties covering the spectral range of 4000–550 cm−1 were acquired to establish discriminant models and to select optimal spectral variables. K nearest neighbors (KNN), support vector machine (SVM), and SSAE models were built using full spectra. The SSAE model performed better than the KNN and SVM models and obtained a classification accuracy 99.4% in the calibration set and 97.9% in the prediction set. Then, three algorithms, principal component analysis loading (PCA-loading), competitive adaptive reweighted sampling (CARS), and stacked sparse auto-encoder guided backward (SSAE-GB), were used to select 39, 300, and 38 optimal wavenumbers, respectively. The KNN and SVM models were built based on optimal wavenumbers. Most of the optimal wavenumbers-based models performed slightly better than the all wavenumbers-based models. The performance of the SSAE-GB was better than the other two from the perspective of the accuracy of the discriminant models and the number of optimal wavenumbers. The results of this study showed that the FTIR spectroscopic technique combined with the SSAE algorithm could be adopted in the identification of the orchid varieties.

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