scholarly journals Detection of Invisible Damages in ‘Rojo Brillante’ Persimmon Fruit at Different Stages Using Hyperspectral Imaging and Chemometrics

Foods ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 2170
Author(s):  
Sandra Munera ◽  
Alejandro Rodríguez-Ortega ◽  
Nuria Aleixos ◽  
Sergio Cubero ◽  
Juan Gómez-Sanchis ◽  
...  

The main cause of flesh browning in ‘Rojo Brillante’ persimmon fruit is mechanical damage caused during harvesting and packing. Innovation and research on nondestructive techniques to detect this phenomenon in the packing lines are necessary because this type of alteration is often only seen when the final consumer peels the fruit. In this work, we have studied the application of hyperspectral imaging in the range of 450–1040 nm to detect mechanical damage without any external symptoms. The fruit was damaged in a controlled manner. Later, images were acquired before and at 0, 1, 2 and 3 days after damage induction. First, the spectral data captured from the images were analysed through an algorithm based on principal component analysis (PCA). The aim was to automatically separate intact and damaged fruit, and to detect the damage in the PC images when present. With this algorithm, 90.0% of intact fruit and 90.8% of damaged fruit were correctly detected. A model based on partial least squares—discriminant analysis (PLS-DA), was later calibrated using the mean spectrum of the pixels detected as damaged, to determine the moment when the fruit was damaged. The model differentiated fruit corresponding correctly to 0, 1, 2 and 3 days after damage induction, achieving a total accuracy of 99.4%.

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2342
Author(s):  
Corentin Martens ◽  
Olivier Debeir ◽  
Christine Decaestecker ◽  
Thierry Metens ◽  
Laetitia Lebrun ◽  
...  

Recent works have demonstrated the added value of dynamic amino acid positron emission tomography (PET) for glioma grading and genotyping, biopsy targeting, and recurrence diagnosis. However, most of these studies are based on hand-crafted qualitative or semi-quantitative features extracted from the mean time activity curve within predefined volumes. Voxelwise dynamic PET data analysis could instead provide a better insight into intra-tumor heterogeneity of gliomas. In this work, we investigate the ability of principal component analysis (PCA) to extract relevant quantitative features from a large number of motion-corrected [S-methyl-11C]methionine ([11C]MET) PET frames. We first demonstrate the robustness of our methodology to noise by means of numerical simulations. We then build a PCA model from dynamic [11C]MET acquisitions of 20 glioma patients. In a distinct cohort of 13 glioma patients, we compare the parametric maps derived from our PCA model to these provided by the classical one-compartment pharmacokinetic model (1TCM). We show that our PCA model outperforms the 1TCM to distinguish characteristic dynamic uptake behaviors within the tumor while being less computationally expensive and not requiring arterial sampling. Such methodology could be valuable to assess the tumor aggressiveness locally with applications for treatment planning and response evaluation. This work further supports the added value of dynamic over static [11C]MET PET in gliomas.


2015 ◽  
Vol 13 (1) ◽  
Author(s):  
Pawel Konieczynski ◽  
Agnieszka Arceusz ◽  
Marek Wesolowski

AbstractThe aim of the studies was to establish relationships between flavonoids and elements important for human health. Therefore, total contents of flavonoids and phosphorus were determined by UV/Vis methods, flavonoids by HPLC, and Ca, Mg, Fe, Mn, Zn and Cu by FAAS in 68 infusions of medicinal herbs. Total flavonoids content in the aqueous extracts were in the range of 0.26 - 16.40 mg per 100 mL. The mean flavonoid contents (in mg per 100 mL of aqueous extract) were 2.24, 2.01, 1.83, 1.88 for rutin, myricetin, quercetin and kaempferol, respectively. The concentrations of Ca, Mg, P were determined in mg per 100 mL, and of Fe, Mn, Zn and Cu in μg per 100 mL. Total content of flavonoids was weakly correlated with quercetin (r = 0.41), kaempferol (r = 0.53), Cu (r = 0.43), and Ca (r = -0.30). Statistically significant correlations were also found among Cu, Ca, Mn, Zn and Fe. Cluster analysis grouped the studied herbs based on total flavonoids, also four flavonoids and essential elements contents, extracted from the whole population of herbs Sambuci flos, Betulae folium, and Sylibi mariani semen. Principal component analysis confirmed these findings and enabled identification of quercetin, kaempferol, Cu and Fe as the factors responsible for differentiation of the studied material.


2021 ◽  
Vol 45 (2) ◽  
pp. 235-244
Author(s):  
A.S. Minkin ◽  
O.V. Nikolaeva ◽  
A.A. Russkov

The paper is aimed at developing an algorithm of hyperspectral data compression that combines small losses with high compression rate. The algorithm relies on a principal component analysis and a method of exhaustion. The principal components are singular vectors of an initial signal matrix, which are found by the method of exhaustion. A retrieved signal matrix is formed in parallel. The process continues until a required retrieval error is attained. The algorithm is described in detail and input and output parameters are specified. Testing is performed using AVIRIS data (Airborne Visible-Infrared Imaging Spectrometer). Three images of differently looking sky (clear sky, partly clouded sky, and overcast skies) are analyzed. For each image, testing is performed for all spectral bands and for a set of bands from which high water-vapour absorption bands are excluded. Retrieval errors versus compression rates are presented. The error formulas include the root mean square deviation, the noise-to-signal ratio, the mean structural similarity index, and the mean relative deviation. It is shown that the retrieval errors decrease by more than an order of magnitude if spectral bands with high gas absorption are disregarded. It is shown that the reason is that weak signals in the absorption bands are measured with great errors, leading to a weak dependence between the spectra in different spatial pixels. A mean cosine distance between the spectra in different spatial pixels is suggested to be used to assess the image compressibility.


2018 ◽  
Vol 4 (12) ◽  
pp. 144 ◽  
Author(s):  
Qian Yang ◽  
Shen Sun ◽  
William Jeffcoate ◽  
Daniel Clark ◽  
Alison Musgove ◽  
...  

Diabetic foot ulcers are a major complication of diabetes and present a considerable burden for both patients and health care providers. As healing often takes many months, a method of determining which ulcers would be most likely to heal would be of great value in identifying patients who require further intervention at an early stage. Hyperspectral imaging (HSI) is a tool that has the potential to meet this clinical need. Due to the different absorption spectra of oxy- and deoxyhemoglobin, in biomedical HSI the majority of research has utilized reflectance spectra to estimate oxygen saturation (SpO2) values from peripheral tissue. In an earlier study, HSI of 43 patients with diabetic foot ulcers at the time of presentation revealed that ulcer healing by 12 weeks could be predicted by the assessment of SpO2 calculated from these images. Principal component analysis (PCA) is an alternative approach to analyzing HSI data. Although frequently applied in other fields, mapping of SpO2 is more common in biomedical HSI. It is therefore valuable to compare the performance of PCA with SpO2 measurement in the prediction of wound healing. Data from the same study group have now been used to examine the relationship between ulcer healing by 12 weeks when the results of the original HSI are analyzed using PCA. At the optimum thresholds, the sensitivity of prediction of healing by 12 weeks using PCA (87.5%) was greater than that of SpO2 (50.0%), with both approaches showing equal specificity (88.2%). The positive predictive value of PCA and oxygen saturation analysis was 0.91 and 0.86, respectively, and a comparison by receiver operating characteristic curve analysis revealed an area under the curve of 0.88 for PCA compared with 0.66 using SpO2 analysis. It is concluded that HSI may be a better predictor of healing when analyzed by PCA than by SpO2.


2006 ◽  
Vol 2 (S240) ◽  
pp. 567-570
Author(s):  
M. Zejda ◽  
Z. Mikulášek ◽  
M. Wolf ◽  
P. Svoboda

AbstractWe analyzed a new photometry of this well-known Algol-like eclipsing binary together with old photoelectric measurements with the aim of better understanding of its orbital period changes and short-time light variations modulating the mean light curve. The analysis has been done by the new method based on the combination of the principal component analysis and robust regression. New spectroscopic observations and radial-velocity curve are also presented.


1981 ◽  
Vol 32 (5) ◽  
pp. 691 ◽  
Author(s):  
PN Fox ◽  
AJ Rathjen

A combination of statistical techniques was used to present useful information for breeders concerning the 197.5 Interstate Wheat Variety Trial. Grouping of sites was similar for all techniques, but was shown most clearly by the principal component analysis. Within three of the four groups of sites there was strong similarity between members. Some groups included widely geographically separated sites, which suggests that in the final stages of varietal testing, it might be possible to use widely separated sites as an alternative to testing over several years within a region. One group dominated the overall mean yields of the trial because it included more sites and because these sites were more uniform than sites within other groups. This domination, illustrated by regression and ranking techniques, may reduce the value to industry of the Interstate Wheat Variety Trials if these sites are not representative of extensive areas of wheat production. The differences in relative performances of varieties between sites could not be related either to differences in the mean yields at these sites or to edaphic or climatic variables. The need for such analysis of each year's data from the Interstate Wheat Variety Trials is stressed.


2021 ◽  
Author(s):  
Chibuike Chiedozie Ibebuchi

Abstract This study examined the separability of circulation types (CTs) classified from the application of principal component analysis (PCA) to the T-mode matrix (variable is time series and observation is grid points) of a climatic field that explains atmospheric circulation; in addition to the uncertainty introduced on (i) the probability of occurrence, (ii) the mean shape of the CTs, (iii) the trend in the annual frequency of occurrence, (iv) the frequency distribution of the CTs, by using varying threshold values within the range of 0.2–0.35 to assign days to a given CT. The study region is Africa, south of the equator. Some large clusters were classified with most days in the analysis period assigned to them; these classes are interpreted as the dominant states of the atmosphere and generally, their existence results in the poor separability of the CTs since their features overlap with other CTs. Qualitatively, the choice of the threshold values within the defined range has little or no influence on the overall structure of the probability of occurrence of the CTs, the mean shape of the CTs, and the year-to-year variations in the annual occurrence of the CTs. However, it significantly impacts the frequency distribution of the CTs and the statistical significance of the trend in the annual occurrence of the CTs. Stringent threshold values within the defined range might benefit studies that aim to isolate days when specific CTs are most expressed and analyze their mechanism using composite maps, without focus on the frequency distribution and annual occurrence of the CTs. Overall, for the study region, lower threshold values within the defined range might be recommended since relatively, they do not tend to further constrain the probability of group membership, and equally seem to reveal the mechanisms that might be consistent when a given CT occurred regardless of the strength of its signal at a given time.


2021 ◽  
Vol 48 (5) ◽  
pp. 1-11
Author(s):  
P.O. Akporhuarho ◽  
O. Iriakpe

The study aimed at explaining objectively the relationship between morphologic traits of two breeds of pigs (Large-white and Duroc) using principal component analysis to determine the body size of grower pigs of two different breeds with a view of identifying components that best define body conformation. Body weight and five biometric variables namely head length, body length, body girth, ham length and ear length. The descriptive statistics showed that the mean body weight of Large-white was 13.14kg while the body measurements were 24.61cm, 71.35cm, 65.12cm, 43.13cm and 21.94cm for head length, body length, body girth, ham length and ear length respectively at 5 – 24 weeks of age. The mean body weight of Duroc was 12.87kg while the body measurements were 23.70cm, 57.93cm, 47.93cm, 22.90cm, 19.26cm for head length, body length, body girth, ham length and ear length respectively. The coefficient of correlation ranges from 0.08-0.424 and 0.01-0.402 for Large-white and Duroc respectively. The association between and were the highest for Duroc, body length r=0.402 and Large-white, body girth 0.424. Two components were identified for Large-white while those of Duroc were three components. The ratios of variance were 53.55 and 71.07% for Large-white and Duroc, respectively. The first factor in each case accounted for the biggest percentage of the total variation, and was designated the general size, the other factors (indices of body shape) offer forms of variation independent of the general size. The principal component based regression models which were chosen for selecting animals for optimal balance accounted for 58 and 76% of the variation in the body weight for Large-white and Duroc respectively. The study concluded that the use of principal component analysis techniques tends to explore the interdependence in the original five parameters measured: head length, body length, body girth, ham length and ear length of Large-white and Duroc     L'étude explique objectivement la relation entre les traits morphologiques de deux races de porcs (gros blanc et de Duroc) à l'aide d'une analyse de composants principaux afin de déterminer la taille du corps des porcs de producteurs de deux races différentes en vue d'identifier les composants qui définissent le mieux la conformation corporelle. Poids corporel et cinq variables biométriques, nommément longueur de la tête, longueur du corps, circonférence du corps, longueur du jambon et longueur de l'oreille. Les statistiques descriptives ont montré que le poids corporel moyen de gros blanc était de 13,14 kg tandis que les mesures du corps étaient de 24,61 cm, 71,35 cm, 65,12 cm, 43,13 cm et 21,94 cm pour la longueur de la tête, la longueur du corps, la circonférence du corps, la longueur du jambon et la longueur de l'oreille respectivement à 5 - 24 semaines. Le poids corporel moyen de Duroc était de 12,87 kg tandis que les mesures du corps étaient de 23,70 cm, 57,93 cm, 47,93 cm, 22,90 cm, 19,26 cm pour la longueur de la tête, la longueur du corps, la circonférence du corps, la longueur du jambon et la longueur de l'oreille respectivement. Le coefficient de corrélation varie de 0,08 à 0,424 et de 0,01 à 0,402 pour les gros blancs et Duroc respectivement. L'association entre et étaient les plus élevées pour Duroc, la longueur du corps R = 0,402 et de gros blancs, la circonférence du corps 0,424. Deux composants ont été identifiés pour les gros blancs tandis que ceux de Duroc étaient trois composants. Les ratios de variance étaient respectivement de 53,55 et 71,07% pour les gros blancs et Duroc. Le premier facteur de chaque cas représentait le plus gros pourcentage de la variation totale et a été désigné la taille générale, les autres facteurs (indices de la forme du corps) offrent des formes de variation indépendantes de la taille générale. Les principaux modèles de régression basés sur les composants choisis pour sélectionner des animaux pour un solde optimal représentaient 58 et 76% de la variation du poids corporel pour les grands blancs et Duroc respectivement. L'étude a conclu que l'utilisation de techniques d'analyse des composants principaux a tendance à explorer l'interdépendance dans les cinq paramètres d'origines mesurées: longueur de la tête, longueur du corps, circonférence corporelle, longueur du jambon et longueur de l'oreille de grosse blanc et de Duroc


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