FT-IR-based quantitative analysis strategy for target adulterant in fish oil multiply adulterated with terrestrial animal lipid.

The interference of nontarget adulterant on FT-IR-based target adulterant quantitative analysis was explored and a sequential strategy was proposed to improve the prediction accuracy of the quantitative analysis model. Based on the FT-IR data of fish oil adulterated with terrestrial animal lipid, PLS and PLS-DA results show that quantitative analysis modeled by multiple and single adulteration data do not apply to each other; quantitative models based on the fusion of single and multiple adulteration data were established and showed a low quantitative analysis precision (higher RSD); and the sensitivity and specificity of discrimination analysis for multiply and singly adulterated fish oils both all exceed 0.910. To enhance the detection accuracy, a sequential strategy was proposed; identifying singly or multiply adulterated fish oil and then quantifying the content of adulterant was considered an efficient approach.

Fresh, freeze-dried or cell wall samples: Which is the most appropriate to determine chemical, structural and rheological variations during apple processing using ATR-FTIR spectroscopy?

Attenuated total reflectance Fourier transform spectroscopy (ATR-FTIR) was applied on fresh (NF), freeze-dried (FD) and cell wall materials (AIS) of raw and processed apples. These samples prepared from 36 apple sets and the corresponding 72 purees, issued from different varieties, agricultural practices, storage periods and processing conditions, were used to build models including exploratory analysis, supervised classification and multivariate calibration. Fresh and freeze-dried samples presented similar fingerprint spectral variations due to processing. ATR-FTIR directly on fresh purees satisfactorily predicted textural properties such as particle average size and volume (RPD > 3.0), while freeze-drying improved assessment of chemical (RPD > 3.2) and rheological (RPD > 3.1) parameters using partial least-squares regression. The assessment of texture and macrocomponents of purees can be obtained with a limited sample preparation. For research applications because of a need of sample preparation, changes of cell wall composition during fruit processing could be assessed in relationship with pectin degradation.

Simple distinction of grapevine (Vitis vinifera L.) genotypes by direct ATR-FTIR.

The identification of grapevine (Vitis vinifera L.) genotypes is conventionally a laborious activity that must be carried out by specialized staff. In this work a novel and simple method for differentiation of grapevine genotypes is presented. Direct measurements of leaves by attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) combined with chemometric methods were used for classification of six genotypes (five varieties and a pair of clones), viz. Cinsault, Gewurztraminer (clone 643), Moscatel de Alejandría, País, Pinot Noir (French clone 777), Pinot Noir (local clone 'Valdivieso'). These were successfully classified and identified through supervised pattern recognition methods such as soft independent modeling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA). The error rate for spectra classification of test sets by both models was 0.08. The results demonstrate the advantages of using ATR-FTIR as a rapid and non-destructive tool that achieves accurate grapevine genotype differentiation.

Economic value of information from an alert system on physiological imbalance in fresh cows.

Physiological imbalance is an abnormal physiological condition that cannot be directly observed but is assumed to precede subclinical and clinical diseases in the beginning of lactation. Alert systems to detect the physiological imbalance in a cow using Fourier transform mid-infrared spectroscopy in milk have been developed. The objective of this study was to estimate the value of information provided from such system with different indicator accuracies, herd prevalence and prices. A decision tree was created to model the probabilities of detection and associated costs of test outcome, intervention and occurrence of disease. We assumed that the negative effect of physiological imbalance was the development of subclinical ketosis and that this negative effect was prevented by drenching the cows with propylene glycol for 5 days. We simulated the economic impact of subclinical ketosis mediated through physiological imbalance to be $194 per case. The results showed that if the alert system was highly accurate (Se = 0.99/Sp = 0.99), and the prevalence of physiological imbalance was 30 %, the value of information provided from the system is $19 per cow-year. In case the prevalence is 5 % or 50 %, the value of information is $3 and $13, respectively. These estimates for the value do not cover the capital costs and operational costs of the alert system. This study furthermore clearly demonstrated that in order to estimate the value of information correctly, it is important to consider that drenching all cows and not drenching any of the cows are the two relevant alternative options in the absence of the alert system. In conclusion, the decision tree and sensitivity analysis developed in this study show that final economic results are highly variable to the prevalence of physiological imbalance and highest at an intermediate prevalence. Other relevant factors are the costs associated with drenching and the cost associated with treating false positives and not treating false negatives. In addition, this study highlights the benefits of simulation to pinpoint where additional information is needed to further quantify the economic value and required accuracy of an indication-based intervention system.

Predictive Modeling of the In Vitro Responses of Preosteoblastic MC3T3-E1 Cells on Polymeric Surfaces Using Fourier Transform Infrared Spectroscopy.

Biomaterials' surface properties elicit diverse cellular responses in biomedical and biotechnological applications. Predicting the cell behavior on a polymeric surface is an ongoing challenge due to its complexity. This work proposes a novel modeling methodology based on attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy. Spectra were collected on wetted polymeric surfaces to incorporate both surface chemistry and information on water-polymer interactions. Results showed that predictive models built with spectra from wetted surfaces ("wet spectra") performed much better than models built using spectra acquired from dry surfaces ("dry spectra"), suggesting that the water-polymer interaction is critically important to the prediction of subsequent cell behavior. The best model was seen to predict total area of focal adhesions with coefficient of determination for prediction (R2P) of 0.94 and root-mean-square errors of prediction (RMSEP) of 4.03 µm2 when tested on an independent experimental set. This work offers new insights into our understanding of cell-biomaterial interactions. The presence of carboxyl groups in polymers promoted larger adhesion areas, yet the formation of carbonyl-to-water interaction decreased adhesion areas. Surface wettability, which was related to the water-polymer interaction, was proven to highly influence cell adhesion. The good predictive ability opens new possibilities for high throughput monitoring of cell attachment on polymeric substrates.

Application of ATR-FTIR spectroscopy and chemometrics for the discrimination of human bone remains from different archaeological sites in Turkey.

Examining diagenetic parameters such as the organic carbonate contents and the crystallinity of bone apatite quantify the post-mortem alteration of bone. Burial conditions are one of the factors that can influence the diagenesis process. We studied the changes to the organic and mineral components and crystallinity of human bone remains from five Medieval sites in Turkey: Hakemi Use, Komana, Iznik, Oluz Höyük and Tasmasor using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) and principal component analysis (PCA). Analysis of spectral band ratios related to organic and mineral components of bone demonstrated differences in the molecular content in the skeletal remains from the five sites. In order to examine the degree of carbonation of a phosphate matrix, curve-fitting procedures were applied to the carbonate band. We found that the infrared crystallinity index appears to not be sensitive to carbonate content at room temperature for the bone remains studied here. The recrystallization process in bone remains behaved differently among the archaeological sites. The results demonstrate that the burial environments differently affect the organic and mineral components of archaeological bone remains.

Model-based correction algorithm for Fourier Transform infrared microscopy measurements of complex tissue-substrate systems.

Model-based algorithms have recently attracted much attention for data pre-processing in tissue mapping and imaging by Fourier transform infrared micro-spectroscopy (FTIR). Their versatility, robustness and computational performance enabled the improvement of spectral quality by mitigating the impact of scattering and fringing in FTIR spectra of chemically homogeneous biological systems. However, to date, no comprehensive algorithm has been optimized and automated for large-area FTIR imaging of histologically complex tissue samples. Herein, for the first time, we propose a unique, integrated and fully-automated Multiple Linear Regression Multi-Reference (MLR-MR) method for correcting linear baseline effects due to diffuse scattering, for compensating substrate thickness inhomogeneity and accounting for sample chemical heterogeneity in FTIR images. In particular, the algorithm uses multiple-reference spectra for histologically heterogeneous biological samples. The performance of the procedure was demonstrated for FTIR imaging of chemically complex rat brain frontal cortex tissue samples, mounted onto Ultralene® films. The proposed MLR-MR correction algorithm allows the efficient retrieval of "pure" absorbance spectra and greatly improves the histological fidelity of FTIR imaging data, as compared with the one-reference approach. In addition, the MLR-MR algorithm here presented opens up the possibility for extracting information on substrate thickness variability, thus enabling the indirect evaluation of its topography. As a whole, the MLR-MR procedure can be easily extended to more complex systems for which Mie scattering effects must also be eliminated.

A modified mid-level data fusion approach on electronic nose and FT-NIR data for evaluating the effect of different storage conditions on rice germ shelf life.

Evaluating the possibility of extending shelf life of rice germ (a by-product of rice milling process) by reducing water activity in combination with storage atmosphere packaging, without any heat treatment, is the aim of the present study. Samples at different water activities (0.55, 0.45 and 0.36) were packed in air, argon or under vacuum, and stored at 27 °C for 150 days. To the aim, a non-targeted approach was applied by means of an FT-NIR spectrometer in reflectance with a rotating sample holder and a portable electronic nose, equipped with 10 non-specific sensors. For understanding the impact of the factors under study on the rice germ shelf life, a modified mid-level data fusion approach was applied to enhance the information most correlated with time. Moreover, Principal Component Analysis was applied on fused data to follow samples evolution during storage and identify different clusters according to the storage conditions. The rice germ case study allowed to better understand the information captured by the non-specific sensors: a 2D correlation map was developed combining the e-nose data with the NIR spectral information, highlighting relationships among NIR absorption bands and classes of chemical compounds inducing e-nose responses. A data fusion approach highlighted the importance of water activity on rice germ storage, while no interesting differences were ascribable to storage atmosphere packaging systems. In terms of correlation, the sensors could be divided in two groups, negatively inter-correlated: sensors ascribable to aromatic compounds (WC) and correlated with the NIR band around 4800-4900 cm-1 (N-H bending of primary amides, typical for peptides coming from protein hydrolysis); broad-range response sensors (WS), linked with the NIR band at 5128 cm-1 (second overtone of CO stretching of esters).

Drug checking services at music festivals and events in a Canadian setting.

OBJECTIVES: Drug checking is a harm reduction intervention that allows for identification of drug composition. The objective of the study was to assess drug market components and concordance between expected substance reported by clients and results from point-of-care drug checking at music festivals and events in British Columbia. METHODS: From July to September 2018, we provided drug checking services at four events using combination Fourier Transform Infrared (FTIR) spectroscopy and fentanyl immunoassay strips. We measured concordance between expected substance as reported by clients to the results from the FTIR/fentanyl immunoassay strip and tracked unexpected adulterants. RESULTS: In total, 336 checks were completed. Most samples were expected by clients to be psychedelics (69.3%) or stimulants (19.6%). Of the 233 psychedelic samples, 169 (72.5%) contained the expected, unadulterated substance, and 27 (11.6%) contained additional contaminants. Of 66 stimulant samples, 41 (62.1%) contained expected substance, while 24 (36.4%) contained additional contaminants. Unexpected adulterants such as fentanyl, levamisole, and phenacetin were also found, in addition to several novel psychoactive substances. DISCUSSION: We found a large proportion of substances that contained unexpected adulterants. Our findings highlight the value of continued drug checking and will be helpful in designing future harm reduction interventions in similar contexts.

Developing infrared spectroscopic detection for stratifying brain tumour patients: glioblastoma multiforme vs. lymphoma.

Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients' average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques - random forest, partial least squares-discriminant analysis and support vector machine - have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy.

FTIR-based hierarchical modeling for prediction of average molecular weights of protein hydrolysates.

In the presented study, Fourier-transform infrared (FTIR) spectroscopy is used to predict the average molecular weight of protein hydrolysates produced from protein-rich by-products from food industry using commercial enzymes. Enzymatic protein hydrolysis is a well-established method for production of protein-rich formulations, recognized for its potential to valorize food-processing by-products. The monitoring of such processes is still a significant challenge as the existing classical analytical methods are not easily applicable to industrial setups. In this study, we are reporting a generic FTIR-based approach for monitoring the average molecular weights of proteins during enzymatic hydrolysis of by-products from the food industry. A total of 885 hydrolysate samples from enzymatic protein hydrolysis reactions of poultry and fish by-products using different enzymes were studied. FTIR spectra acquired from dry-films of the hydrolysates were used to build partial least squares regression (PLSR) models. The most accurate predictions were obtained using a hierarchical PLSR approach involving supervised classification of the FTIR spectra according to raw material quality and enzyme used in the hydrolysis process, and subsequent local regression models tuned to specific enzyme-raw material combinations. The results clearly underline the potential of using FTIR for monitoring protein sizes during enzymatic protein hydrolysis in industrial settings, while also paving the way for measurements of protein sizes in other applications.

Combining Fourier Transform Mid-Infrared Spectroscopy with Chemometric Methods to Detect Adulterations in Milk Powder.

Adulteration is one of the major concerns among all the quality problems of milk powder. Soybean flour and rice flour are harmless adulterations in the milk powder. In this study, mid-infrared spectroscopy was used to detect the milk powder adulterated with rice flour or soybean flour and simultaneously determine the adulterations content. Partial least squares (PLS), support vector machine (SVM) and extreme learning machine (ELM) were used to establish classification and regression models using full spectra and optimal wavenumbers. ELM models using the optimal wavenumbers selected by principal component analysis (PCA) loadings obtained good results with all the sensitivity and specificity over 90%. Regression models using the full spectra and the optimal wavenumbers selected by successive projections algorithm (SPA) obtained good results, with coefficient of determination (R2) of calibration and prediction all over 0.9 and the predictive residual deviation (RPD) over 3. The classification results of ELM models and the determination results of adulterations content indicated that the mid-infrared spectroscopy was an effective technique to detect the rice flour and soybean flour adulteration in the milk powder. This study would help to apply mid-infrared spectroscopy to the detection of adulterations such as rice flour and soybean flour in real-world conditions.

High throughput FT-MIR indirect analysis of sugars and acids in watermelon.

Indirect measurements of taste-related compounds are required when a high number of samples has to be analyzed in a short period of time, with a minimum cost. For this purpose, FT-MIR partial least square (PLS) regression models for the prediction of total soluble solids, sugars and organic acids have been developed using three sample sets including breeding lines and commercial varieties of watermelon. Specific models with excellent performance were obtained only for sugars. Nevertheless, a general model supposed a compromise between the best and worse models and offered %RMSEP values of 11.3%, 11.1% and 11.7% for fructose, glucose and sucrose respectively. The model was applied to the selection of high content samples (selection pressure 20% and 30%) obtaining good sensitivity levels and mean percentile of selected samples close to the expected values (100% sensitivity). The robustness of FT-MIR models was assessed with predictions of external assays, obtaining reasonable performances.

The use of Fourier-transform infrared spectroscopy to characterize connective tissue components in skeletal muscle of Atlantic cod (Gadus morhua L.).

In the present study, Fourier-transform infrared spectroscopy (FTIR) is investigated as a method to measure connective tissue components that are important for the quality of Atlantic cod filets (Gadus morhua L.). The Atlantic cod used in this study originated from a feeding trial, which found that fish fed a high starch diet contained relative more collagen type I, while fish fed a low starch (LS) diet contained relative more glycosaminoglycans (GAGs) in the connective tissue. FTIR spectra of pure commercial collagen type I and GAGs were acquired to identify spectral markers and compare them with FTIR spectra and images from connective tissue. Using principal component analysis, high and LS diets were separated due to collagen type I in the spectral region 1800 to 800 cm-1 . The spatial distribution of collagen type I and GAGs were further investigated by FTIR imaging in combination with immunohistochemistry. Pixel-wise correlation images were calculated between preprocessed connective tissue images and preprocessed pure components spectra of collagen type I and GAGs, respectively. For collagen, the FTIR images reveal a collagen distribution that closely resembles the collagen distribution as imaged by immunohistochemistry. For GAGs, the concentration is very low. Still, the FTIR images detect the most GAGs rich regions.

Authentication of cocoa bean shells by near- and mid-infrared spectroscopy and inductively coupled plasma-optical emission spectroscopy.

The aim of this study was to evaluate the efficacy of a multi-analytical approach for origin authentication of cocoa bean shells (CBS). The overall chemical profiles of CBS from different origins were characterized using diffuse reflectance near-infrared spectroscopy (NIRS) and attenuated total reflectance mid-infrared spectroscopy (ATR-FT-IR) for molecular composition identification, as well as inductively coupled plasma-optical emission spectroscopy (ICP-OES) for elemental composition identification. Exploratory chemometric techniques based on Principal Component Analysis (PCA) were applied to each single technique for the identification of systematic patterns related to the geographical origin of samples. A combination of the three techniques proved to be the most promising approach to establish classification models. Partial Least Squares-Discriminant Analysis modelling of fused PCA scores of three independent models was used and compared with single technique models. Improved classification of CBS samples was obtained using the fused model. Satisfactory classification rates were obtained for Central African samples with an accuracy of 0.84.

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods.

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated.

Geographic identification of Boletus mushrooms by data fusion of FT-IR and UV spectroscopies combined with multivariate statistical analysis.

Boletus griseus and Boletus edulis are two well-known wild-grown edible mushrooms which have high nutrition, delicious flavor and high economic value distributing in Yunnan Province. In this study, a rapid method using Fourier transform infrared (FT-IR) and ultraviolet (UV) spectroscopies coupled with data fusion was established for the discrimination of Boletus mushrooms from seven different geographical origins with pattern recognition method. Initially, the spectra of 332 mushroom samples obtained from the two spectroscopic techniques were analyzed individually and then the classification performance based on data fusion strategy was investigated. Meanwhile, the latent variables (LVs) of FT-IR and UV spectra were extracted by partial least square discriminant analysis (PLS-DA) and two datasets were concatenated into a new matrix for data fusion. Then, the fusion matrix was further analyzed by support vector machine (SVM). Compared with single spectroscopic technique, data fusion strategy can improve the classification performance effectively. In particular, the accuracy of correct classification of SVM model in training and test sets were 99.10% and 100.00%, respectively. The results demonstrated that data fusion of FT-IR and UV spectra can provide higher synergic effect for the discrimination of different geographical origins of Boletus mushrooms, which may be benefit for further authentication and quality assessment of edible mushrooms.

Application of reflectance spectroscopies (FTIR-ATR & FT-NIR) coupled with multivariate methods for robust in vivo detection of begomovirus infection in papaya leaves.

Nucleic acid & serology based methods have revolutionized plant disease detection, however, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic infection, in addition, they need at least 1-2days for sample harvesting, processing, and analysis. In this study, two reflectance spectroscopies i.e. Near Infrared reflectance spectroscopy (NIR) and Fourier-Transform-Infrared spectroscopy with Attenuated Total Reflection (FT-IR, ATR) coupled with multivariate exploratory methods like Principle Component Analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been deployed to detect begomovirus infection in papaya leaves. The application of those techniques demonstrates that they are very useful for robust in vivo detection of plant begomovirus infection. These methods are simple, sensitive, reproducible, precise, and do not require any lengthy samples preparation procedures.

Use of FTIR spectroscopy and PCA-LDC analysis to identify cancerous lesions within the human colon.

Colorectal cancer constitutes 33% of all cancer morbidity, so the research of the new methods for colorectal cancer diagnosis and chemotherapy monitoring is gaining its momentum. Diagnostic instruments are being sought, which enable the detection of single malignant cells based on the analysis of tissue material potentially reusable at further stages of diagnostic management. The most common approach to tissue specimen processing is paraffin-embedding. Yet, paraffin may cause background noise in spectroscopic measurements with the wavenumber ranging between 900cm-1 and 3500cm-1. However, the study by Depciuch et al. (2016) proved that appropriate specimen processing and paraffin-embedding technique as well as a strict measurement methodology may eliminate paraffin vibrations. As a result, spectroscopic measurements may become a reliable and precise method for the diagnosis and treatment monitoring in patients with colorectal cancer as long as the high standards of specimen processing are maintained. Chemotherapy is the main medical treatment in colorectal cancer. Unfortunately, the absence of tools which enable monitoring its efficacy leads to the partial response or non-response frequently seen in affected patients. Hence, diagnostic instruments are also being sought capable of monitoring treatment efficacy so as to enable early changes of chemotherapy regimen thus increasing the chance of cure. The paper aims at comparing the results of FTIR (Fourier Transform Infrared) spectroscopy in several types of colon tissue: healthy colon, cancerous colon, post-chemotherapy colon and healthy surgical margin of colon cancer sample. The obtained FTIR spectra along with the Principal Component Analysis-Linear Discriminant Analysis (PCA-LDC) as well as bandwidth analysis of the primary amide region revealed some differences between the spectra of healthy tissues as compared to cancerous tissues (pre- or post-chemotherapy). Apart from confirming that FTIR spectroscopy is a good source of information on the composition of analysed samples, this fact supports its application as a tool to facilitate understanding the pathophysiology of various conditions and to monitor efficacy of chemotherapy in cancer patients.

Simultaneous Fitting of Absorption Spectra and Their Second Derivatives for an Improved Analysis of Protein Infrared Spectra.

Infrared spectroscopy is a powerful tool in protein science due to its sensitivity to changes in secondary structure or conformation. In order to take advantage of the full power of infrared spectroscopy in structural studies of proteins, complex band contours, such as the amide I band, have to be decomposed into their main component bands, a process referred to as curve fitting. In this paper, we report on an improved curve fitting approach in which absorption spectra and second derivative spectra are fitted simultaneously. Our approach, which we name co-fitting, leads to a more reliable modelling of the experimental data because it uses more spectral information than the standard approach of fitting only the absorption spectrum. It also avoids that the fitting routine becomes trapped in local minima. We have tested the proposed approach using infrared absorption spectra of three mixed α/ß proteins with different degrees of spectral overlap in the amide I region: ribonuclease A, pyruvate kinase, and aconitase.