A method for non-destructive determination of cocoa bean fermentation levels based on terahertz hyperspectral imaging.

Fermentation of cocoa is a key process to obtain aromatic chocolate products from raw cocoa beans. Hitherto, the levels of fermentation in cocoa are determined using destructive techniques, for example by a cut-test to manually observe the colour inside the beans, or by quantifying ammonia nitrogen (NH3) in the cocoa powder. In this paper, we present the use of Terahertz hyperspectral imaging as a new way to non-destructively analyse and detect fermented cocoa beans. The study analysed two sets of twenty-two cocoa bean samples with different levels of fermentation from two producers in Brazil. A correlation between fermentation conditions and the outcome results of their THz measurements was observed.

Comparison of Spectroscopic Techniques Combined with Chemometrics for Cocaine Powder Analysis.

Spectroscopic techniques combined with chemometrics are a promising tool for analysis of seized drug powders. In this study, the performance of three spectroscopic techniques [Mid-InfraRed (MIR), Raman and Near-InfraRed (NIR)] was compared. In total, 364 seized powders were analyzed and consisted of 276 cocaine powders (with concentrations ranging from 4 to 99 w%) and 88 powders without cocaine. A classification model (using Support Vector Machines [SVM] discriminant analysis) and a quantification model (using SVM regression) were constructed with each spectral dataset in order to discriminate cocaine powders from other powders and quantify cocaine in powders classified as cocaine positive. The performances of the models were compared with gas chromatography coupled with mass spectrometry (GC-MS) and gas chromatography with flame-ionization detection (GC-FID). Different evaluation criteria were used: number of false negatives (FNs), number of false positives (FPs), accuracy, root mean square error of cross-validation (RMSECV) and determination coefficients (R2). Ten colored powders were excluded from the classification data set due to fluorescence background observed in Raman spectra. For the classification, the best accuracy (99.7%) was obtained with MIR spectra. With Raman and NIR spectra, the accuracy was 99.5% and 98.9%, respectively. For the quantification, the best results were obtained with NIR spectra. The cocaine content was determined with a RMSECV of 3.79% and a R2 of 0.97. The performance of MIR and Raman to predict cocaine concentrations was lower than NIR, with RMSECV of 6.76% and 6.79%, respectively and both with a R2 of 0.90. The three spectroscopic techniques can be applied for both classification and quantification of cocaine, but some differences in performance were detected. The best classification was obtained with MIR spectra. For quantification, however, the RMSECV of MIR and Raman was twice as high in comparison with NIR. Spectroscopic techniques combined with chemometrics can reduce the workload for confirmation analysis (e.g., chromatography based) and therefore save time and resources.

Determination of the olive maturity index of intact fruits using image analysis.

In this work, the maturity index of different samples of olives was objectively assessed by image analysis obtained through machine vision, in which algorithms of color-based segmentation and operators to detect edges were used. This method allows a fast, automatic and objective prediction of olive maturity index. This prediction value was compared to maturity index (MI), generally used by olive oil industry, based on the subjective visual determination of color of fruit skin and flesh. Machine vision was also applied to the automatic estimation of size and weight of olive fruits. The proposed system was tested to obtain a good performance in the classification of the fruit in batches. When applied to several olive samples, the maturity index predicted by machine vision was in close agreement with the maturity index of fruits visually estimated, values that are currently used as standards. The evaluation of weight of fruit also provided good results (R(2) = 0.91). These results obtained by image analysis can be used as a useful method for the classification of olives at the reception in olive mill, allowing a better quality control of the production process.

Evaluation of the overall quality of olive oil using fluorescence spectroscopy.

The fluorescence spectra of some olive oils were examined in their natural and oxidised state, with wavelength range emissions of 300-800 nm and 300-400 nm used as excitation radiation. The fluorescence emissions were measured and an assessment was made of the relationship between them and the main quality parameters of olive oils, such as peroxide value, K232, K270 and acidity. These quality parameters (peroxide value, K232, K270 and acidity) are determined by laboratory methods, which though not too sophisticated, they are required solvents and materials as well as time consuming and sample preparation; there is a need for rapid analytical techniques and a low-cost technology for olive oil quality control. The oxidised oils studied had a strong fluorescence band at 430-450 nm. Extra virgin olive oil gave a different but interesting fluorescence spectrum, composed of three bands: one low intensity doublet at 440 and 455 nm; one strong band at 525 nm; and one of medium intensity at 681 nm. The band at 681 nm was identified as the chlorophyll band. The band at 525 nm was derived, at least partially, from vitamin E. The results presented demonstrate the ability of the fluorescence technique, combined with multivariate analysis, to characterise olive oils on the basis of all the quality parameters studied. Prediction models were obtained using various methods, such as partial least squares (PLS), N-way PLS (N-PLS) and external validation, in order to obtain an overall evaluation of oil quality. The best results were obtained for predicting K270 with a root mean square (RMS) prediction error of 0.08 and a correlation coefficient obtained with the external validation of 0.924. Fluorescence spectroscopy facilitates the detection of virgin olive oils obtained from defective or poorly maintained fruits (high acidity), fruits that are highly degraded in the early stages (with a high peroxide value) and oils in advanced stages of oxidation, with secondary oxidation compounds (high K232 and K270). The results indicate the potential of a spectrofluorimetric method combined with multivariate analysis to differentiate, and even quantify, the levels of oil quality. The proposed methodology could be used to accelerate analysis, is inexpensive and allows a comprehensive assessment to be made of olive oil quality.

Infrared machine vision system for the automatic detection of olive fruit quality.

External quality is an important factor in the extraction of olive oil and the marketing of olive fruits. The appearance and presence of external damage are factors that influence the quality of the oil extracted and the perception of consumers, determining the level of acceptance prior to purchase in the case of table olives. The aim of this paper is to report on artificial vision techniques developed for the online estimation of olive quality and to assess the effectiveness of these techniques in evaluating quality based on detecting external defects. This method of classifying olives according to the presence of defects is based on an infrared (IR) vision system. Images of defects were acquired using a digital monochrome camera with band-pass filters on near-infrared (NIR). The original images were processed using segmentation algorithms, edge detection and pixel value intensity to classify the whole fruit. The detection of the defect involved a pixel classification procedure based on nonparametric models of the healthy and defective areas of olives. Classification tests were performed on olives to assess the effectiveness of the proposed method. This research showed that the IR vision system is a useful technology for the automatic assessment of olives that has the potential for use in offline inspection and for online sorting for defects and the presence of surface damage, easily distinguishing those that do not meet minimum quality requirements.

A portable Raman sensor for the rapid discrimination of olives according to fruit quality.

In the real marketplace, providing high-quality olive oil is important from the perspective of both consumers and producers. Quality control should meet all requirements in the production process, from farm to packaging. The quality of olive oil can be affected by several factors, including agricultural techniques, seasonal conditions, farming systems, maturity, method and duration of storage, and process technology. The quality of oil produced also depends largely on the quality of the olives. In an enterprise aimed at producing high-quality oils, olives with defects ('ground'; i.e., fallen to the ground) should be separated from healthy fruit ('sound'; i.e., collected directly from the tree), because a very small portion of low-quality fruit can ruin the whole batch. The fruit falls partly because of its maturation process, but also because of pest and disease attack or weather conditions (strong wind). Fruit that has fallen to the ground can suffer a rapid deterioration in quality. Currently, the separation of fruits is based mainly on visual inspection or information provided by the farmer. These are not very reliable procedures. Methods using analytical parameters to characterize the oil, such as acidity and peroxide value, can be applied, but they require a lot of time and materials. Alternative techniques are therefore needed for the rapid and inexpensive discrimination of olives as part of a quality control strategy. The work described here aims to determine the potential of low-resolution Raman spectroscopy for the discrimination of olives before the oil processing stage in order to detect whether they have been collected directly from the tree (i.e., healthy fruit) or not. Low-resolution Raman spectroscopy was applied together with multivariate procedures to achieve this aim. PCA was used to find natural clusters in the data. Supervised classification methods were then applied: Soft Independent Modeling of Class Analogy (SIMCA), PLS Discriminate Analysis (PLS-DA) and K-nearest neighbors (KNN). The best results were obtained using the KNN method, with prediction abilities of 100% for 'sound' and 97% for 'ground' in an independent validation set. These results demonstrated the potential of a portable Raman instrument for detecting good quality olives before the oil processing stage, by developing models that could be applied before this stage, thus contributing to an overall improvement in quality control.