Portable NIR Spectroscopy to Simultaneously Trace Honey Botanical and Geographical Origins and Detect Syrup Adulteration

Fraudulent practices concerning honey are growing fast and involve misrepresentation of origin and adulteration. Simple and feasible methods for honey authentication are needed to ascertain honey compliance and quality. Working on a robust dataset and simultaneously investigating honey traceability and adulterant detection, this study proposed a portable FTNIR fingerprinting approach combined with chemometrics. Multifloral and unifloral honey samples (n = 244) from Spain and Sardinia (Italy) were discriminated by botanical and geographical origin. Qualitative and quantitative methods were developed using linear discriminant analysis (LDA) and partial least squares (PLS) regression to detect adulterated honey with two syrups, consisting of glucose, fructose, and maltose. Botanical and geographical origins were predicted with 90% and 95% accuracy, respectively. LDA models discriminated pure and adulterated honey samples with an accuracy of over 92%, whereas PLS allows for the accurate quantification of over 10% of adulterants in unifloral and 20% in multifloral honey

Effect of the irrigation method and genotype on the bioaccumulation of toxic and trace elements in rice

The total concentration of three toxic elements (As, Cd and Pb) and five oligoelements (Cu, Mn, Mo, Ni and Se) has been determined using an original and completely validated ICP-MS method. This was applied to rice grains from 26 different genotypes cultivated in the same soil and irrigated with the same water in three different ways: by the traditional continuous flooding (CF) and by two intermittent methods, the sprinkler irrigation (SP) and the periodical saturation of the soil (SA). The adoption of SP hugely minimizes the average amounts of almost all elements in kernels (−98% for As, −90% for Se and Mn, −60% for Mo, −50% for Cd and Pb), with the only exception of Ni, whose concentration increases the average amount found in the CF rice by 7.5 times. Also SA irrigation is able to reduce the amounts of As, Mo and Pb in kernels but it significantly increases the amounts of Mn, Ni and – mainly - Cd. Also the nature of the genotype determined a wide variability of data within each irrigation method. Genotypes belonging to Indica subspecies are the best bioaccumulators of elements in both CF and SP methods and, never, the worst bioaccumulators for any element/irrigation method combination. In the principal component analysis, PC1 can differentiate samples irrigated by SP by those irrigated by CF and SA, whereas PC2 provides differentiation of CF samples by SA samples. When looking at the loading plot Ni is negatively correlated to the majority of the other elements, except Cu and Cd having negative loadings on PC2. These results allow to envisage that a proper combination of the irrigation method and the nature of rice genotype might be a very valuable tool in order to successfully achieve specific objectives of food safety or the attainment of functional properties

Portable NIR spectroscopy to simultaneously trace honey botanical and geographicl origins and detect syrup adulteration.

Fraudulent practices concerning honey are growing fast and involve misrepresentation of origin and adulteration. Simple and feasible methods for honey authentication are needed to ascertain honey compliance and quality. Working on a robust dataset and simultaneously investigating honey traceability and adulterant detection, this study proposed a portable FTNIR fingerprinting approach combined with chemometrics. Multifloral and unifloral honey samples (n = 244) from Spain and Sardinia (Italy) were discriminated by botanical and geographical origin. Qualitative and quantitative methods were developed using linear discriminant analysis (LDA) and partial least squares (PLS) regression to detect adulterated honey with two syrups, consisting of glucose, fructose, and maltose. Botanical and geographical origins were predicted with 90% and 95% accuracy, respectively. LDA models discriminated pure and adulterated honey samples with an accuracy of over 92%, whereas PLS allows for the accurate quantification of over 10% of adulterants in unifloral and 20% in multifloral honey

Elemental Fingerprinting Combined with Machine Learning Techniques as a Powerful Tool for Geographical Discrimination of Honeys from Nearby Regions

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machinelearning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictor

Elemental fingerprinting combined with machine learning techniques as a powerful tool for geographical discrimination of honeys from nearby regions

Discrimination of honey based on geographical origin is a common fraudulent practice and is one of the most investigated topics in honey authentication. This research aims to discriminate honeys according to their geographical origin by combining elemental fingerprinting with machine-learning techniques. In particular, the main objective of this study is to distinguish the origin of unifloral and multifloral honeys produced in neighboring regions, such as Sardinia (Italy) and Spain. The elemental compositions of 247 honeys were determined using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The origins of honey were differentiated using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Random Forest (RF). Compared to LDA, RF demonstrated greater stability and better classification performance. The best classification was based on geographical origin, achieving 90% accuracy using Na, Mg, Mn, Sr, Zn, Ce, Nd, Eu, and Tb as predictors

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Sprinkler irrigation in the production of safe rice by soils heavily polluted by arsenic and cadmium

Among the factors affecting the bioaccumulation of As and Cd in rice, a key role is played by the irrigation methods. The sprinkler irrigation (SP), optimized for rice in Sardinia, Italy, applied to several rice genotypes over many years has produced no differences in yields in comparison to what observed using the traditional continuous flooding irrigation method (CF). Because all the previous SP trials have been performed just on one, unpolluted soil, the principal aim of this study is to ascertain the effectiveness of SP to simultaneously minimize the bioaccumulation of As and Cd in rice grain even in soils severely polluted by As and/or Cd. Hence, a Carnise rice genotype was cultivated in an open field in: i) an unpolluted soil; ii) a soil polluted with 55 mg kg−1 of As; iii) a soil polluted with 40 mg kg−1 of Cd; iv) a soil polluted with 50 mg kg−1 of As and 50 mg kg−1 of Cd. In the worst condition of pollution, the amounts of total As and Cd measured in the kernels using a fully validated ICP-MS method is 90 ± 10 μg kg−1 and 50 ± 20 μg kg−1, respectively, i.e. less than 50% and the 25% of the maximum concentration set for these elements in rice by the European Community (200 μg kg−1 for the inorganic As and the total amount of Cd, respectively). SP might represent a simple and valuable tool able to produce safe rice also from soils where the traditional irrigation might produce inedible rice only

Icp-ms determination of 23 elements of potential health concern in liquids of e-cigarettes. Method development, validation, and application to 37 real samples

The lack of interest in the determination of toxic elements in liquids for electronic cigarettes (e-liquids) has so far been reflected in the scarce number of accurate and validated analytical methods devoted to this aim. Since the strong matrix effects observed for e-liquids constitute an exciting analytical challenge, the main goal of this study was to develop and validate an ICP-MS method aimed to quantify 23 elements in 37 e-liquids of different flavors. Great attention has been paid to the critical phases of sample pre-treatment, as well as to the optimization of the ICP-MS conditions for each element and of the quantification. All samples exhibited a very low amount of the elements under investigation. Indeed, the sum of their average concentration was of ca. 0.6 mg kg−1. Toxic elements were always below a few tens of a μg per kg−1 and, very often, their amount was below the relevant quantification limits. Tobacco and tonic flavors showed the highest and the lowest concentration of elements, respectively. The most abundant elements came frequently from propylene glycol and vegetal glycerin, as confirmed by PCA. A proper choice of these substances could further decrease the elemental concentration in e-liquids, which are probably barely involved as potential sources of toxic elements inhaled by vapers