Non-destructive assessment of olive fruit ripening by portable near infrared spectroscopy

Extraction Systems and Analytical Techniques for Food Phenolic Compounds: A Review

Phenolic compounds are highly valuable food components due to their potential utilisation as natural bioactive and antioxidant molecules for the food, cosmetic, chemical, and pharmaceutical industries. For this purpose, the development and optimisation of efficient extraction methods is crucial to obtain phenolic-rich extracts and, for some applications, free of interfering compounds. It should be accompanied with robust analytical tools that enable the standardisation of phenolic-rich extracts for industrial applications. New methodologies based on both novel extraction and/or analysis are also implemented to characterise and elucidate novel chemical structures and to face safety, pharmacology, and toxicity issues related to phenolic compounds at the molecular level. Moreover, in combination with multivariate analysis, the extraction and analysis of phenolic compounds offer tools for plant chemotyping, food traceability and marker selection in omics studies. Therefore, this study reviews extraction techniques applied to recover phenolic compounds from foods and agri-food by-products, including liquid-liquid extraction, solid-liquid extraction assisted by intensification technologies, solid-phase extraction, and combined methods. It also provides an overview of the characterisation techniques, including UV-Vis, infra-red, nuclear magnetic resonance, mass spectrometry and others used in minor applications such as Raman spectroscopy and ion mobility spectrometry, coupled or not to chromatography. Overall, a wide range of methodologies are now available, which can be applied individually and combined to provide complementary results in the roadmap around the study of phenolic compounds.

Handheld Devices for Food Authentication and Their Applications: A Review

This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.

Optimal management of oil content variability in olive mill batches by NIR spectroscopy

Total oil content (OC) is one of the main parameters used to characterize the whole of olives entering a commercial mill, quantified by the total fresh weight of the lot and the oil concentration (%) assessed in a representative sample on olive paste, by means of chemical extraction. Nuclear magnetic resonance (NMR) and NIR spectroscopy are alternative methods even at individual olives. This work evaluates several strategies to calibrate precise NIR models for the estimation of the total OC. To this end, 278 olives were analysed covering whole season variability in terms of olive fresh-weight and the corresponding OC by chemical extraction in 31 batches. The average spectra from hyperspectral NIR images (1003-2208 nm) were computed for each fruit and the actual OC (g) of those olives determined by NMR (0.09 to 1.29 g with a precision of 0.017 g). According to the results, current batch based assessment of the OC (Soxhlet, %) in mills only reproduces 44% of the underlying heterogeneity, despite being the factory standard. The incorporation of individual NIR spectra (278) to the 31 Soxhlet values of the batches allows a 67% explanation of the OC (%) of olives. When estimating OC (g) gathering individual fresh weight and the estimation of oil concentration in olives, a standard error of prediction of 0.061 g is reached (r² = 0.93), a precision value that approaches the potential limit according to the NMR reference (0.017 g).

Advances in Non-Destructive Early Assessment of Fruit Ripeness towards Defining Optimal Time of Harvest and Yield Prediction-A Review

Global food security for the increasing world population not only requires increased sustainable production of food but a significant reduction in pre- and post-harvest waste. The timing of when a fruit is harvested is critical for reducing waste along the supply chain and increasing fruit quality for consumers. The early in-field assessment of fruit ripeness and prediction of the harvest date and yield by non-destructive technologies have the potential to revolutionize farming practices and enable the consumer to eat the tastiest and freshest fruit possible. A variety of non-destructive techniques have been applied to estimate the ripeness or maturity but not all of them are applicable for in situ (field or glasshouse) assessment. This review focuses on the non-destructive methods which are promising for, or have already been applied to, the pre-harvest in-field measurements including colorimetry, visible imaging, spectroscopy and spectroscopic imaging. Machine learning and regression models used in assessing ripeness are also discussed.

Novel technologies for monitoring the in-line quality of virgin olive oil during manufacturing and storage

The quality of virgin olive oil is related to the agronomic conditions of the olive fruits and the process variables of the production process. Nowadays, food markets demand better products in terms of safety, health and organoleptic properties with competitive prices. Innovative techniques for process control, inspection and classification have been developed in order to to achieve these requirements. This paper presents a review of the most significant sensing technologies which are increasingly used in the olive oil industry to supervise and control the virgin olive oil production process. Throughout the present work, the main research studies in the literature that employ non-invasive technologies such as infrared spectroscopy, computer vision, machine olfaction technology, electronic tongues and dielectric spectroscopy are analysed and their main results and conclusions are presented. These technologies are used on olive fruit, olive slurry and olive oil to determine parameters such as acidity, peroxide indexes, ripening indexes, organoleptic properties and minor components, among others. © 2016 Society of Chemical Industry.

A review on the applications of portable near-infrared spectrometers in the agro-food industry

Industry has created the need for a cost-effective and nondestructive quality-control analysis system. This requirement has increased interest in near-infrared (NIR) spectroscopy, leading to the development and marketing of handheld devices that enable new applications that can be implemented in situ. Portable NIR spectrometers are powerful instruments offering several advantages for nondestructive, online, or in situ analysis: small size, low cost, robustness, simplicity of analysis, sample user interface, portability, and ergonomic design. Several studies of on-site NIR applications are presented: characterization of internal and external parameters of fruits and vegetables; conservation state and fat content of meat and fish; distinguishing among and quality evaluation of beverages and dairy products; protein content of cereals; evaluation of grape ripeness in vineyards; and soil analysis. Chemometrics is an essential part of NIR spectroscopy manipulation because wavelength-dependent scattering effects, instrumental noise, ambient effects, and other sources of variability may complicate the spectra. As a consequence, it is difficult to assign specific absorption bands to specific functional groups. To achieve useful and meaningful results, multivariate statistical techniques (essentially involving regression techniques coupled with spectral preprocessing) are therefore required to extract the information hidden in the spectra. This work reviews the evolution of the use of portable near-infrared spectrometers in the agro-food industry.

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.

Oil accumulation in intact olive fruits measured by near infrared spectroscopy-acousto-optically tunable filter

BACKGROUND

A field experiment was conducted to test the reliability of the near infrared spectroscopy (NIR)-acousto-optically tunable filter (AOTF) method to measure mesocarp oil content in vivo against nuclear magnetic resonance (NMR) determinations using three different olive cultivars at different stages of ripening.

RESULTS

In the partial least squares model carried out for the cultivar 'Arbequina', the coefficient of determination in calibration (R(2)c) was 0.991, while the coefficient of determination in cross-validation (R(2)cv) was 0.979. For the cultivar 'Frantoio' the indexes were 0.982 and 0.971, respectively; while for the cultivar 'Leccino' R(2)c was 0.977 and R(2)cv was 0.965. Finally, for the combined model (sum of the three varieties) these indexes were 0.921 and 0.903, respectively. The residual predictive deviation (RPD) ratio was insufficient for the predictive model of cultivar 'Leccino' only (1.98), whereas in the other cases the RPD ratios were completely sufficient, within the estimation range over 2.5-3 (2.61 in the global model, and 4.23 in the cultivar 'Frantoio'), or in describing a large capacity with values greater than 5, as in the cultivar 'Arbequina' (9.58).

CONCLUSION

NIR-AOTF spectroscopy proved to be a novel, rapid and reliable method to monitor the oil accumulation process in intact olive fruits in the field. The innovative approach of coupling NIR and NMR technologies opens up new scenarios for determining the optimal time for harvesting olive trees to obtain maximum oil production.

Feasible application of a portable NIR-AOTF tool for on-field prediction of phenolic compounds during the ripening of olives for oil production

Olive fruits of three different cultivars (Moraiolo, Dolce di Andria, and Nocellara Etnea) were monitored during ripening up to harvest, and specific and total phenols were measured by HPLC (High Pressure Liquid Chromatography). On the same olive samples (n = 450), spectral detections were performed using a portable NIR (Near Infrared)-AOTF (Acousto Optically Tunable Filter) device in diffuse reflectance mode (1100-2300 nm). Prediction models were developed for the main phenolic compounds (e.g., oleuropein, verbascoside, and 3,4-DHPEA-EDA) and total phenols using Partial Least Squares (PLS). Internal cross-validation (leave-one-out method) was applied for calibration and prediction models developed on the data sets relative to each single cultivar. Validation of the models obtained as the sum of the three sample sets (total phenols, n = 162; verbascoside, n = 162; oleuropein, n = 148; 3,4-DHPEA-EDA, n = 162) were performed by external sets of data. Obtained results in term of R(2) (in calibration, prediction and cross-validation) ranged between 0.930 and 0.998, 0.874-0.942, and 0.837-0.992, respectively. Standard errors in calibration (RMSEC), cross-validation (RMSECV), and prediction (RMSEP) were calculated obtaining minimum error in prediction of 0.68 and maximum of 6.33 mg/g. RPD ratios (SD/SECV) were also calculated as references of the model effectiveness. This work shows how NIR-AOTF can be considered a feasible tool for the on-field and nondestructive measurement of specific and total phenols in olives for oil production.