scholarly journals Absorbance Spectroscopy of Heads, Hearts and Tails Fractions in Fruit Spirits

Beverages ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 21
Author(s):  
Jens Bohn ◽  
Simon Roj ◽  
Luis Hoppert ◽  
Daniel Heller ◽  
Daniel Einfalt
Keyword(s):  
Uv Spectroscopy ◽  
Low Cost ◽  
Area Under The Curve ◽  
Principal Component ◽  
Zero Point ◽  
Uv Spectra ◽  
Absorbance Spectroscopy ◽  
Clear Differentiation ◽  
Distillate Fractions ◽  
Fruit Spirits

There is a large economic interest to characterize heads, hearts and tails fractions during fruit spirit distillation by simple, fast, low-volume and low-cost analytical methods. This study evaluated the potential of ultraviolet (UV)-visible-infrared spectroscopy (230–1000 nm) to characterize and differentiate these distillate fractions. Heads, hearts and tails fractions of 10 different fruit spirits were separated by sensory evaluation and investigated by absorbance spectroscopy. Principal component analysis indicated that UV spectroscopy at a wavelength range from 230 to 310 nm had the highest potential to differentiate all three distillate fractions. While all tails fractions showed significantly different UV spectra, a clear differentiation between heads and hearts fractions was limited. However, an additional UV spectroscopy of 100 mL subfractions sampled during the shift from heads to hearts in three additional distillations did reveal significant differences. The calculated integrals of the according best-fit trendline functions of the spectra indicated a trend towards reduced area-under-the-curve and zero-point values during the shift. This could be a new lead to implement an analytical method for in-line process control during fruit spirit production.

scholarly journals Classification of Lampung robusta Specialty Coffee According to Differences in Cherry Processing Methods Using UV Spectroscopy and Chemometrics

Agriculture ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 109
Author(s):  
Diding Suhandy ◽  
Meinilwita Yulia
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Uv Spectroscopy ◽  
Low Cost ◽  
Principal Component ◽  
Support Vector ◽  
Processing Methods ◽  
Linear Discriminant ◽  
Specialty Coffee ◽  
Supervised Classification Methods

The postharvest processing factors including cherry processing methods highly influence the final quality of coffee beverages, especially in the composition of several coffee metabolites such as glucose, fructose, the amino acid (glutamic acid), and chlorogenic acids (CGA) as well as trigonelline contents. In this research, UV spectroscopy combined with chemometrics was used to classify a ground roasted Lampung robusta specialty coffee according to differences in the cherry processing methods. A total of 360 samples of Lampung robusta specialty coffee with 1 g of weight for each sample from three different cherry processing methods were prepared as samples: 100 samples of pure dry coffee (DRY), 100 samples of pure semi-dry coffee (SMD), 100 samples of pure wet coffee (WET) and 60 samples of adulterated coffee (ADT) (SMD coffee was adulterated with DRY and WET coffee). All samples were extracted using a standard protocol as explained by previous works. A low-cost benchtop UV-visible spectrometer (Genesys™ 10S UV-Vis, Thermo Scientific, Waltham, MA, USA) was utilized to obtain UV spectral data in the interval of 190–400 nm using the fast scanning mode. Using the first three principal components (PCs) with a total of 93% of explained variance, there was a clear separation between samples. The samples were clustered into four possible groups according to differences in cherry processing methods: dry, semi-dry, wet, and adulterated. Four supervised classification methods, partial least squares–discriminant analysis (PLS-DA), principal component analysis–linear discriminant analysis (PCA-LDA), linear discriminant analysis (LDA) and support vector machine classification (SVMC) were selected to classify the Lampung robusta specialty coffee according to differences in the cherry processing methods. PCA-LDA is the best classification method with 91.7% classification accuracy in prediction. PLS-DA, LDA and SVMC give an accuracy of 56.7%, 80.0% and 85.0%, respectively. The present research suggested that UV spectroscopy combining with chemometrics will be highly useful in Lampung robusta specialty coffee authentication.

Comparison of UV- and Derivative-Spectrophotometric and HPTLC UVDensitometric Methods for the Determination of Amrinone and Milrinone in Bulk Drugs

2020 ◽  
Vol 16 (3) ◽  
pp. 246-253
Author(s):  
Marcin Gackowski ◽  
Marcin Koba ◽  
Stefan Kruszewski
Keyword(s):  
Thin Layer ◽  
Thin Layer Chromatography ◽  
High Performance ◽  
Low Cost ◽  
Uv Spectra ◽  
Derivative Spectrophotometry ◽  
Therapeutic Monitoring ◽  
Bulk Drug ◽  
Layer Chromatography ◽  
Standard Solutions

Background: Spectrophotometry and thin layer chromatography have been commonly applied in pharmaceutical analysis for many years due to low cost, simplicity and short time of execution. Moreover, the latest modifications including automation of those methods have made them very effective and easy to perform, therefore, the new UV- and derivative spectrophotometry as well as high performance thin layer chromatography UV-densitometric (HPTLC) methods for the routine estimation of amrinone and milrinone in pharmaceutical formulation have been developed and compared in this work since European Pharmacopoeia 9.0 has yet incorporated in an analytical monograph a method for quantification of those compounds. Methods: For the first method the best conditions for quantification were achieved by measuring the lengths between two extrema (peak-to-peak amplitudes) 252 and 277 nm in UV spectra of standard solutions of amrinone and a signal at 288 nm of the first derivative spectra of standard solutions of milrinone. The linearity between D252-277 signal and concentration of amironone and 1D288 signal of milrinone in the same range of 5.0-25.0 μg ml/ml in DMSO:methanol (1:3 v/v) solutions presents the square correlation coefficient (r2) of 0,9997 and 0.9991, respectively. The second method was founded on HPTLC on silica plates, 1,4-dioxane:hexane (100:1.5) as a mobile phase and densitometric scanning at 252 nm for amrinone and at 271 nm for milrinone. Results: The assays were linear over the concentration range of 0,25-5.0 μg per spot (r2=0,9959) and 0,25-10.0 μg per spot (r2=0,9970) for amrinone and milrinone, respectively. The mean recoveries percentage were 99.81 and 100,34 for amrinone as well as 99,58 and 99.46 for milrinone, obtained with spectrophotometry and HPTLC, respectively. Conclusion: The comparison between two elaborated methods leads to the conclusion that UV and derivative spectrophotometry is more precise and gives better recovery, and that is why it should be applied for routine estimation of amrinone and milrinone in bulk drug, pharmaceutical forms and for therapeutic monitoring of the drug.

scholarly journals Evaluation of indigenous aromatic rice cultivars from sub-Himalayan Terai region of India for nutritional attributes and blast resistance

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Debayan Mondal ◽  
Prudveesh Kantamraju ◽  
Susmita Jha ◽  
Gadge Sushant Sundarrao ◽  
Arpan Bhowmik ◽  
...  
Keyword(s):  
Nutritive Value ◽  
Amylose Content ◽  
Blast Resistance ◽  
Starch Content ◽  
Low Cost ◽  
Principal Component ◽  
Blast Disease ◽  
Basmati Rice ◽  
Phenotypic Traits ◽  
Rice Cultivars

AbstractIndigenous folk rice cultivars often possess remarkable but unrevealed potential in terms of nutritional attributes and biotic stress tolerance. The unique cooking qualities and blissful aroma of many of these landraces make it an attractive low-cost alternative to high priced Basmati rice. Sub-Himalayan Terai region is bestowed with great agrobiodiversity in traditional heirloom rice cultivars. In the present study, ninety-nine folk rice cultivars from these regions were collected, purified and characterized for morphological and yield traits. Based on traditional importance and presence of aroma, thirty-five genotypes were selected and analyzed for genetic diversity using micro-satellite marker system. The genotypes were found to be genetically distinct and of high nutritive value. The resistant starch content, amylose content, glycemic index and antioxidant potential of these genotypes represented wide variability and ‘Kataribhog’, ‘Sadanunia’, ‘Chakhao’ etc. were identified as promising genotypes in terms of different nutritional attributes. These cultivars were screened further for resistance against blast disease in field trials and cultivars like ‘Sadanunia’, ‘T4M-3-5’, ‘Chakhao Sampark’ were found to be highly resistant to the blast disease whereas ‘Kalonunia’, ‘Gobindabhog’, ‘Konkanijoha’ were found to be highly susceptible. Principal Component analysis divided the genotypes in distinct groups for nutritional potential and blast tolerance. The resistant and susceptible genotypes were screened for the presence of the blast resistant pi genes and association analysis was performed with disease tolerance. Finally, a logistic model based on phenotypic traits for prediction of the blast susceptibility of the genotypes is proposed with more than 80% accuracy.

scholarly journals GC-MS Based Identification of the Volatile Components of Six Astragalus Species from Uzbekistan and Their Biological Activity

Plants ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 124
Author(s):  
Haidy A. Gad ◽  
Nilufar Z. Mamadalieva ◽  
Stefan Böhmdorfer ◽  
Thomas Rosenau ◽  
Gokhan Zengin ◽  
...  
Keyword(s):  
Radical Scavenging ◽  
Reducing Power ◽  
Principal Component ◽  
Inhibitory Effect ◽  
Maximum Activity ◽  
Volatile Components ◽  
Drug Candidates ◽  
Predominant Component ◽  
Clear Differentiation

The compositions of volatile components in the aerial parts of six Astragalus species, namely A. campylotrichus (Aca), A. chiwensis (Ach), A. lehmannianus (Ale), A. macronyx (Ama), A. mucidus (Amu) and A. sieversianus (Asi), were investigated using gas chromatograph-mass spectrometry (GC-MS) analysis. Ninety-seven metabolites were identified, accounting for 73.28, 87.03, 74.38, 87.93, 85.83, and 91.39% of Aca, Ach, Ale, Ama, Amu and Asi whole oils, respectively. Sylvestrene was the most predominant component in Asi, Amu and Ama, with highest concentration in Asi (64.64%). In addition, (E)-2-hexenal was present in a high percentage in both Ale and Ach (9.97 and 10.1%, respectively). GC-MS based metabolites were subjected to principal component analysis (PCA) and hierarchal cluster analysis (HCA) to explore the correlations between the six species. The PCA score plot displayed clear differentiation of all Astragalus species and a high correlation between the Amu and Ama species. The antioxidant activity was evaluated in vitro using various assays, phosphomolybdenum (PM), 2,2 diphenyl-1-picryl-hydrazyl-hydrate (DPPH), 2,2-azino bis (3-ethylbenzothiazoline-6-sulphonic acid) (ABTS), cupric reducing antioxidant capacity (CUPRAC), ferric reducing power (FRAP) and ferrous ion chelation (FIC) assays. In addition, the potential for the volatile samples to inhibit both acetyl/butyrylcholinesterases (AChE, BChE), α- amylase, α-glucosidase and tyrosinase was assessed. Most of the species showed considerable antioxidant potential in the performed assays. In the DPPH assay, Ama exhibited the maximum activity (24.12 ± 2.24 mg TE/g sample), and the volatiles from Amu exhibited the highest activity (91.54 mgTE/g oil) in the ABTS radical scavenging assay. The effect was more evident in both CUPRAC and FRAP assays, where both Ale and Ama showed the strongest activity in comparison with the other tested species (84.06, 80.28 mgTE/g oil for CUPRAC and 49.47, 49.02 mgTE/g oil for FRAP, respectively). Asi demonstrated the strongest AChE (4.55 mg GALAE/g oil) and BChE (3.61 mg GALAE/g oil) inhibitory effect. Furthermore, the best tyrosinase inhibitory potential was observed for Ale (138.42 mg KAE/g). Accordingly, Astragalus species can be utilized as promising natural sources for many medicinally important components that could be tested as drug candidates for treating illnesses such as Alzheimer’s disease, diabetes mellitus and oxidative stress-related diseases.

scholarly journals End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

2021 ◽  
Vol 7 (2) ◽  
pp. 356-362
Author(s):  
Harry Coppock ◽  
Alex Gaskell ◽  
Panagiotis Tzirakis ◽  
Alice Baird ◽  
Lyn Jones ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Cost ◽  
Area Under The Curve ◽  
Alternative Form ◽  
Economic Damage ◽  
Audio Signals ◽  
Operating Characteristics ◽  
Data Set ◽  
Empirical Performance

BackgroundSince the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution.MethodsThis study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings.ResultsOur model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification.ConclusionThis study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.

scholarly journals Characterization of the Volatile Profile of Cultivated and Wild-Type Italian Celery (Apium graveolens L.) Varieties by HS-SPME/GC-MS

2021 ◽  
Vol 11 (13) ◽  
pp. 5855
Author(s):  
Samantha Reale ◽  
Valter Di Cecco ◽  
Francesca Di Donato ◽  
Luciano Di Martino ◽  
Aurelio Manzi ◽  
...  
Keyword(s):  
Solid Phase ◽  
Principal Component ◽  
Apium Graveolens ◽  
Gas Chromatography Mass Spectrometry ◽  
Bioactive Metabolites ◽  
Chemical Components ◽  
Volatile Profile ◽  
Wild Type ◽  
Multivariate Statistical ◽  
Clear Differentiation

Celery (Apium graveolens L.) is a vegetable belonging to the Apiaceae family that is widely used for its distinct flavor and contains a variety of bioactive metabolites with healthy properties. Some celery ecotypes cultivated in specific territories of Italy have recently attracted the attention of consumers and scientists because of their peculiar sensorial and nutritional properties. In this work, the volatile profiles of white celery “Sedano Bianco di Sperlonga” Protected Geographical Indication (PGI) ecotype, black celery “Sedano Nero di Torricella Peligna” and wild-type celery were investigated using head-space solid-phase microextraction combined with gas-chromatography/mass spectrometry (HS-SPME/GC-MS) and compared to that of the common ribbed celery. Exploratory multivariate statistical analyses were conducted using principal component analysis (PCA) on HS-SPME/GC-MS patterns, separately collected from celery leaves and petioles, to assess similarity/dissimilarity in the flavor composition of the investigated varieties. PCA revealed a clear differentiation of wild-type celery from the cultivated varieties. Among the cultivated varieties, black celery “Sedano Nero di Torricella Peligna” exhibited a significantly different composition in volatile profile in both leaves and petioles compared to the white celery and the prevalent commercial variety. The chemical components of aroma, potentially useful for the classification of celery according to the variety/origin, were identified.

scholarly journals Raman Spectral Signatures of Serum-Derived Extracellular Vesicle-Enriched Isolates May Support the Diagnosis of CNS Tumors

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1407
Author(s):  
Matyas Bukva ◽  
Gabriella Dobra ◽  
Juan Gomez-Perez ◽  
Krisztian Koos ◽  
Maria Harmati ◽  
...  
Keyword(s):  
Lumbar Disc ◽  
Area Under The Curve ◽  
Principal Component ◽  
Extracellular Vesicle ◽  
Classification Performance ◽  
Cns Tumors ◽  
Clinical Samples ◽  
Support Vector ◽  
Serum Samples ◽  
Raman Spectroscopic

Investigating the molecular composition of small extracellular vesicles (sEVs) for tumor diagnostic purposes is becoming increasingly popular, especially for diseases for which diagnosis is challenging, such as central nervous system (CNS) malignancies. Thorough examination of the molecular content of sEVs by Raman spectroscopy is a promising but hitherto barely explored approach for these tumor types. We attempt to reveal the potential role of serum-derived sEVs in diagnosing CNS tumors through Raman spectroscopic analyses using a relevant number of clinical samples. A total of 138 serum samples were obtained from four patient groups (glioblastoma multiforme, non-small-cell lung cancer brain metastasis, meningioma and lumbar disc herniation as control). After isolation, characterization and Raman spectroscopic assessment of sEVs, the Principal Component Analysis–Support Vector Machine (PCA–SVM) algorithm was performed on the Raman spectra for pairwise classifications. Classification accuracy (CA), sensitivity, specificity and the Area Under the Curve (AUC) value derived from Receiver Operating Characteristic (ROC) analyses were used to evaluate the performance of classification. The groups compared were distinguishable with 82.9–92.5% CA, 80–95% sensitivity and 80–90% specificity. AUC scores in the range of 0.82–0.9 suggest excellent and outstanding classification performance. Our results support that Raman spectroscopic analysis of sEV-enriched isolates from serum is a promising method that could be further developed in order to be applicable in the diagnosis of CNS tumors.

scholarly journals An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yuanyuan Xu ◽  
Genke Yang ◽  
Jiliang Luo ◽  
Jianan He
Keyword(s):  
Deep Learning ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Electronic Component ◽  
Support Vector ◽  
Learning Networks ◽  
Component Recognition ◽  
And Performance

Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal 10 − 6   level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance.

Pedometer-Determined Step-Count Guidelines for Afterschool Programs

2012 ◽  
Vol 9 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Michael W. Beets ◽  
Aaron Beighle ◽  
Matteo Bottai ◽  
Laura Rooney ◽  
Fallon Tilley
Keyword(s):  
Operating Characteristic ◽  
Low Cost ◽  
Vigorous Physical Activity ◽  
Area Under The Curve ◽  
High Specificity ◽  
Step Count ◽  
Afterschool Programs ◽  
Policy Goal ◽  
Community Based ◽  
Random Intercept

Background:Policies to require afterschool programs (ASPs, 3 PM to 6 PM) to provide children a minimum of 30 minutes of moderate-to-vigorous physical activity (MVPA) exist. With few low-cost, easy-to-use measures of MVPA available to the general public, ASP providers are limited in their ability to track progress toward achieving this policy-goal. Pedometers may fill this gap, yet there are no step-count guidelines for ASPs linked to 30 minutes of MVPA.Methods:Steps and accelerometer estimates of MVPA were collected concurrently over multiple days on 245 children (8.2 years, 48% boys, BMI-percentile 68.2) attending 3 community-based ASPs. Random intercept logit models and receiver operating characteristic (ROC) analyses were used to identify a threshold of steps that corresponded with attaining 30 minutes of MVPA.Results:Children accumulated an average of 2876 steps (standard error [SE] 79) and 16.1 minutes (SE0.5) of MVPA over 111 minutes (SE1.3) during the ASP. A threshold of 4600 steps provided high specificity (0.967) and adequate sensitivity (0.646) for discriminating children who achieved the 30 minutes of MVPA; 93% of the children were correctly classified. The total area under the curve was 0.919. Children accumulating 4600 steps were 25times more likely to accumulate 30 minutes of MVPA.Conclusions:This step threshold will provide ASP leaders with an objective, low-cost, easy-to-use tool to monitor progress toward policy-related goals.

scholarly journals Evolution of Wireless Sensor Network for Air Quality Measurements

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 342 ◽  
Author(s):  
Patricia Arroyo ◽  
Jesús Lozano ◽  
José Suárez
Keyword(s):  
Air Quality ◽  
Sensor Network ◽  
Low Cost ◽  
Principal Component ◽  
Solar Panels ◽  
Success Rates ◽  
Sampling System ◽  
Air Quality Measurements ◽  
Zigbee Protocol ◽  
Benzene Toluene

This study addresses the development of a wireless gas sensor network with low cost, small size, and low consumption nodes for environmental applications and air quality detection. Throughout the article, the evolution of the design and development of the system is presented, describing four designed prototypes. The final proposed prototype node has the capacity to connect up to four metal oxide (MOX) gas sensors, and has high autonomy thanks to the use of solar panels, as well as having an indirect sampling system and a small size. ZigBee protocol is used to transmit data wirelessly to a self-developed data cloud. The discrimination capacity of the device was checked with the volatile organic compounds benzene, toluene, ethylbenzene, and xylene (BTEX). An improvement of the system was achieved to obtain optimal success rates in the classification stage with the final prototype. Data processing was carried out using techniques of pattern recognition and artificial intelligence, such as radial basis networks and principal component analysis (PCA).

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