Valorization of Olive Pruning Residues through Bioconversion into Edible Mushroom Pleurotus ostreatus (Jacq. Ex Fr.) P. Kumm. (1871) of Improved Nutritional Value

In several Mediterranean countries, olive pruning residues (OLPRs) are abandoned or burned leading to several environmental problems. Valorization of these agrowastes could be a challenge for the primary decomposer Pleurotus ostreatus, turning them into edible biomass. The OLPR was used alone (OLPR), or in mixtures with wheat straw (WS : OLPR 1 : 3 v/v and WS : OLPR 3 : 1 v/v). Mycelial colonization was hastened by 3.7 days in WS : OLPR 1 : 3 (v/v). Yields were comparable to control (WS) in WS : OLPR 3 : 1 (v/v). Organic matter loss decreased with increasing proportions of OLPR in substrates. The nutritional value of mushrooms was improved by lower fat and sodium contents, in WS : OLPR 1 : 3 (v/v) and WS : OLPR 3 : 1 (v/v), and higher total protein, crude fiber, iron, and total carbohydrates contents in WS : OLPR 3 : 1 (v/v), compared with those of control. Polyunsaturated fatty acids, mainly linoleic acid, were the most abundant in mushrooms. Monounsaturated fatty acids increased in mushrooms of the substrates containing OLPR. A good predictive model of partial least square regression analysis showed different relationships of mushroom palmitic, oleic, linolenic, palmitoleic, and stearic acids with substrate composition. Findings suggested the use of OLPR as a supplement to commercial wheat straw and as a tool to reduce the negative impacts of their hazardous disposal on the environment.

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Fatty Acids Predominantly Affect Anti-Hydroxyl Radical Activity and FRAP Value: The Case Study of Two Edible Mushrooms

Antioxidants ◽

10.3390/antiox8100480 ◽

2019 ◽

Vol 8 (10) ◽

pp. 480

Author(s):

Maja Karaman ◽

Kristina Atlagić ◽

Aleksandra Novaković ◽

Filip Šibul ◽

Miroslav Živić ◽

...

Keyword(s):

Fatty Acids ◽

Protocatechuic Acid ◽

Unsaturated Fatty Acids ◽

Partial Least Square ◽

Least Square ◽

Edible Mushrooms ◽

Partial Least Square Regression ◽

Submerged Cultivation ◽

Reducing Ability

Compared to plants, nowadays mushrooms attract more attention as functional foods, due to a number of advantages in manipulating them. This study aimed to screen the chemical composition (fatty acids and phenolics) and antioxidant potential (OH•, 2,2-diphenyl-1-picrylhydrazyl (DPPH•) and ferric reducing ability of plasma (FRAP)) of two edible mushrooms, Coprinus comatus and Coprinellus truncorum, collected from nature and submerged cultivation. Partial least square regression analysis has pointed out the importance of some fatty acids—more precisely, unsaturated fatty acids (UFAs) followed by fatty acids possessing both short (C6:0 and C8:0) and long (C23:0 and C24:0) saturated chains—and phenolic compounds (such as protocatechuic acid, daidzein, p-hydroxybenzoic acid, genistein and vanillic acid) for promising anti-OH•, FRAP and anti-DPPH• activities, respectively. However, other fatty acids (C16:0, C18:0 and C18:3n3) along with the flavonol isorhamnetin are actually suspected to negatively affect (by acting pro-oxidative) the aforementioned parameters, respectively. Taken together, design of new food supplements targeting oxidative stress might be predominantly based on the various UFAs combinations (C18:2n6, C20:1, C20:2, C20:4n6, C22:2, C22:1n9, etc.), particularly if OH• is suspected to play an important role.

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Combined effect of olive pruning residues and spent coffee grounds on Pleurotus ostreatus production, composition, and nutritional value

PLoS ONE ◽

10.1371/journal.pone.0255794 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0255794

Author(s):

Sami Abou Fayssal ◽

Zeina El Sebaaly ◽

Mohammed A. Alsanad ◽

Rita Najjar ◽

Michael Böhme ◽

...

Keyword(s):

Fatty Acids ◽

Wheat Straw ◽

Nutritional Value ◽

Saturated Fatty Acids ◽

International Standards ◽

Combined Effect ◽

Biological Efficiency ◽

Spent Coffee Grounds ◽

Coffee Grounds ◽

Pruning Residues

No previous study assessed the combined effect of olive pruning residues (OLPR) and spent coffee grounds (SCG) on P. ostreatus production and nutritional value. The aim of this study was to determine the capacity of P. ostreatus to degrade lignocellulosic nature of combined OLPR and SCG as well as their resultant nutrient composition. A complete randomized design was adopted with five treatments: S1:100%wheat straw (WS) (control), S2:33%WS+33%SCG+33%OLPR,S3:66%WS+17%SCG+17%OLPR,S4:17%WS+66%SCG+17%OLPR, and S5:17%WS+17%SCG+66%OLPR, and ten replicates per treatment. Substrate’s and mushroom’s composition were analyzed on chemical scale, including fatty acids and heavy metals profiles, following international standards. Only S1, S2, and S3 were productive, with comparable biological yield, economical yield, and biological efficiency. Organic matter loss decreased with increasing proportions of OLPR and SCG. Percentage lignin loss was higher in S1 than in S2 and S3 (53.51, 26.25, and 46.15% respectively). Mushrooms of S3 had some enhanced nutritional attributes compared to control: decrease in fat, increase in protein, increase in monounsaturated fatty acids, and lower zinc accumulation. Lead was less accumulated in S2 than S1 mushrooms. Sodium content of mushroom decreased in S2 and S3. The latter substrates yielded mushrooms with lower polyunsaturated fatty acids (PUFA) and higher saturated fatty acids (SFA) contents. All mushrooms had a valuable PUFA/SFA. This study suggests using OLPR and SCG in low proportions as nutritional supplements to the commercial wheat straw.

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Estimation of Fatty Acids in Intramuscular Fat of Beef by FT-MIR Spectroscopy

Foods ◽

10.3390/foods10010155 ◽

2021 ◽

Vol 10 (1) ◽

pp. 155

Author(s):

María José Beriain ◽

Francisco C. Ibañez ◽

Edurne Beruete ◽

Inmaculada Gómez ◽

Miguel Beruete

Keyword(s):

Fatty Acids ◽

Spectral Response ◽

Intramuscular Fat ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Mir Spectroscopy ◽

Holstein Bulls ◽

Meat Samples ◽

Animal Diet

The aim of this research was to estimate the fatty acid (FA) content of intramuscular fat from beef by Fourier transform mid-infrared (FT-MIR) spectroscopy. Four diets were supplemented in 10% linseed (LS) and/or 2% conjugated linoleic acid (CLA): CON (without L or CLA), LS, CLA, and LS+CLA. For each diet, 12 young Holstein bulls were allocated. The spectral response of the beef samples was analyzed applying FT-MIR spectroscopy (from 400 to 4000 cm−1) and predictive models were developed using partial least square regression with cross-validation. The obtained coefficients (R2) for some FA, such as α-linolenic acid with a R2 = 0.96 or n-3 polyunsaturated fatty acids (n-3 PUFA) with R2 = 0.93, demonstrate that FT-MIR spectroscopy is a valid technique to estimate the content of FA. In addition, samples were correctly classified according to the animal diet using discriminant analysis in the region 3000–1000 cm−1. The obtained results suggest that the FT-MIR spectroscopy could be a viable technique for routine use in quality control because it provides fast and sustainable analysis of FA content. Furthermore, this technique allows the rapid estimation of the FA composition, specifically n-3 PUFA and CLA, of nutritional interest in meat. It also allows the classification of meat samples by the animal diet.

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Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Remote Sensing ◽

10.3390/rs13040641 ◽

2021 ◽

Vol 13 (4) ◽

pp. 641

Author(s):

Gopal Ramdas Mahajan ◽

Bappa Das ◽

Dayesh Murgaokar ◽

Ittai Herrmann ◽

Katja Berger ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Spectral Indices ◽

Learning Models ◽

Leaf Nutrients ◽

Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

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Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

Applied Sciences ◽

10.3390/app11020618 ◽

2021 ◽

Vol 11 (2) ◽

pp. 618

Author(s):

Tanvir Tazul Islam ◽

Md Sajid Ahmed ◽

Md Hassanuzzaman ◽

Syed Athar Bin Amir ◽

Tanzilur Rahman

Keyword(s):

Blood Glucose ◽

Blood Glucose Level ◽

Glucose Level ◽

Quantitative Estimation ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Frequency Noise ◽

Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

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A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽

10.3390/molecules26061546 ◽

2021 ◽

Vol 26 (6) ◽

pp. 1546

Author(s):

Ioanna Dagla ◽

Anthony Tsarbopoulos ◽

Evagelos Gikas

Keyword(s):

Design Of Experiments ◽

High Performance ◽

Similarity Index ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Distance Measures ◽

Linear Regression Models ◽

Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

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Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

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The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study

BMC Neuroscience ◽

10.1186/s12868-021-00608-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Mika Jönsson ◽

Björn Gerdle ◽

Bijar Ghafouri ◽

Emmanuel Bäckryd

Keyword(s):

Cerebrospinal Fluid ◽

Neuropathic Pain ◽

Pain Intensity ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Therapeutic Interventions ◽

Healthy Controls ◽

Inflammatory Profile

Abstract Background Neuropathic pain (NeuP) is a complex, debilitating condition of the somatosensory system, where dysregulation between pro- and anti-inflammatory cytokines and chemokines are believed to play a pivotal role. As of date, there is no ubiquitously accepted diagnostic test for NeuP and current therapeutic interventions are lacking in efficacy. The aim of this study was to investigate the ability of three biofluids - saliva, plasma, and cerebrospinal fluid (CSF), to discriminate an inflammatory profile at a central, systemic, and peripheral level in NeuP patients compared to healthy controls. Methods The concentrations of 71 cytokines, chemokines and growth factors in saliva, plasma, and CSF samples from 13 patients with peripheral NeuP and 13 healthy controls were analyzed using a multiplex-immunoassay based on an electrochemiluminescent detection method. The NeuP patients were recruited from a clinical trial of intrathecal bolus injection of ziconotide (ClinicalTrials.gov identifier NCT01373983). Multivariate data analysis (principal component analysis and orthogonal partial least square regression) was used to identify proteins significant for group discrimination and protein correlation to pain intensity. Proteins with variable influence of projection (VIP) value higher than 1 (combined with the jack-knifed confidence intervals in the coefficients plot not including zero) were considered significant. Results We found 17 cytokines/chemokines that were significantly up- or down-regulated in NeuP patients compared to healthy controls. Of these 17 proteins, 8 were from saliva, 7 from plasma, and 2 from CSF samples. The correlation analysis showed that the most important proteins that correlated to pain intensity were found in plasma (VIP > 1). Conclusions Investigation of the inflammatory profile of NeuP showed that most of the significant proteins for group separation were found in the less invasive biofluids of saliva and plasma. Within the NeuP patient group it was also seen that proteins in plasma had the highest correlation to pain intensity. These preliminary results indicate a potential for further biomarker research in the more easily accessible biofluids of saliva and plasma for chronic peripheral neuropathic pain where a combination of YKL-40 and MIP-1α in saliva might be of special interest for future studies that also include other non-neuropathic pain states.

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Suggestions on the Contribution of Methyl Eugenol and Eugenol to Bay Laurel (Laurus nobilis L.) Essential Oil Preservative Activity through Radical Scavenging

Molecules ◽

10.3390/molecules26082342 ◽

2021 ◽

Vol 26 (8) ◽

pp. 2342

Author(s):

Nikolaos Nenadis ◽

Maria Papapostolou ◽

Maria Z. Tsimidou

Keyword(s):

Essential Oil ◽

Radical Scavenging Activity ◽

Pearson Correlation ◽

Radical Scavenging ◽

Methyl Eugenol ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Positive Contribution ◽

Scavenging Activity

The present study examined the radical scavenging potential of the two benzene derivatives found in the bay laurel essential oil (EO), namely methyl eugenol (MEug) and eugenol (Eug), theoretically and experimentally to make suggestions on their contribution to the EO preservative activity through such a mechanism. Calculation of appropriate molecular indices widely used to characterize chain-breaking antioxidants was carried out in the gas and liquid phases (n-hexane, n-octanol, methanol, water). Experimental evidence was based on the DPPH• scavenging assay applied to pure compounds and a set of bay laurel EOs chemically characterized with GC-MS/FID. Theoretical calculations suggested that the preservative properties of both compounds could be exerted through a radical scavenging mechanism via hydrogen atom donation. Eug was predicted to be of superior efficiency in line with experimental findings. Pearson correlation and partial least square regression analyses of the EO antioxidant activity values vs. % composition of individual volatiles indicated the positive contribution of both compounds to the radical scavenging activity of bay laurel EOs. Eug, despite its low content in bay laurel EOs, was found to influence the most the radical scavenging activity of the latter.

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