Spectral and Hyperspectral Technologies as an Additional Tool to Increase Information on Quality and Origin of Horticultural Crops

Abstract

Nowadays, consumer awareness of the impact of site of origin and method of production on the quality and safety of foods, and particularly of fresh produce, is driving the research towards developing various techniques to assist present certifications, traceability, and audit procedures. With regard to horticultural produce, consumer preferences have shifted to fruit and vegetables, which are healthy and ecologically produced, and toward processed foods having sustainable or social certifications and with sites of origin clearly reported on the label. Some recent studies demonstrate the potentiality of near infrared (NIR) technology (including hyperspectral imaging) for discriminating fresh and processed horticultural products based on their composition, quality attributes, and origin. These studies principally mention that each biological tissue possesses a fingerprint NIR spectrum, which consists of a unique and characteristic pattern of radiation, distinguishing a particular biological tissue from physically and/or chemically different samples. Particularly, recent studies discriminated apples, wine, wheat kernels, and derived flours based on their geographical origins. Spectral information allowed discrimination among growing methods (organic and conventional) for asparagus and strawberry fruits, and among harvest dates for fennels, table grapes, and artichokes. Moreover, information about freshness and storage days after minimal processing can be obtained. Recent literature and original results will be discussed. From our perspective, present results suggest that these techniques may have a potentiality to increase information about product history, but if and only if the variability captured by the classification models is vast in terms of diverse samples belonging to various cultivars, varieties, harvest times, cultural practices, geographical origins, storage conditions, and maturity stages, while being used as a complementary method to the conventional ones―either to make an initial screening of critical features, or to add to the amount of available information. Lacking the inclusion of these parameters could result in good classification results, but the reliability of the classification in this case would be dubious in terms of assessment of the factor contributing towards correct classification.

The Sample, the Spectra and the Maths—The Critical Pillars in the Development of Robust and Sound Applications of Vibrational Spectroscopy

“…Most of these applications are based on the use of vibrational spectroscopy methods (near infrared (NIR), mid infrared (MIR), and Raman, visible (VIS)) in a wide range of samples (e.g., food ingredients, natural products, crops, animal and plant tissues, medical and pharmaceutical applications; etc.) [9][10][11][12][13]. The number of references using words such as "infrared", "NIR", "Raman", "MIR", "hyperspectral", "green analytical methods", "chemometrics" and "multivariate data analysis" [9][10][11][12][13][14][15][16][17], are evidence of this steady increase in the number of applications of these analytical methods.…”

“…In recent years, vibrational spectroscopy has been also considered for its potential as a high throughput phenotyping tool in both animals and plants, where novel applications related with plant breeding and selection, plant nutrition and physiology have been reported in the last 20 years [9][10][11][12][13][14][15]. More recently, vibrational spectroscopy (e.g., NIR, MIR, Raman and hyperspectral imaging systems) techniques have shown their ability to qualitatively (e.g., classifying, identifying, and monitoring) analyse several types of samples (e.g., wine, meat, coffee, condiments, etc.…”

“…More recently, vibrational spectroscopy (e.g., NIR, MIR, Raman and hyperspectral imaging systems) techniques have shown their ability to qualitatively (e.g., classifying, identifying, and monitoring) analyse several types of samples (e.g., wine, meat, coffee, condiments, etc. ), targeting issues related with origin, traceability, and provenance of foods and food ingredients [9][10][11][12][13][14][15][16][17]. Concomitantly, recent developments in hardware (e.g., image techniques, optical sensors, handheld instrumentation, etc.)…”

“…This approach is not entirely strange to the industry where applications of these techniques also attracted an increase in interest from the pharmaceutical, food and beverage industries, etc. [9][10][11][12][13][14][15][16][17]. The main reasons for the increasing use of this approach might be due to the main advantages that these methods and techniques possess when compared with other routine analytical techniques or methods, such as the non-destructive nature of these technologies, minimal or no sample preparation, no chemical reagents required, easy and ready to use instrumentation, and availability of inexpensive and portable devices [9][10][11][12][13][14][15][16][17][18][19][20].…”

“…One of the main analytical advantages of rapid analytical methods or high throughput techniques is that they can evaluate/measure the biochemical and/or chemical characteristics of any given organic compound [9][10][11][12][13][14][15][16][17][18][19][20]. This might be possible as chemical bonds present in the sample vibrate at specific frequencies or wavelengths depending of their mass of the constituent atoms, the shape of the molecule, and the stiffness of the bonds [9][10][11][12][13][14][15][16][17][18][19][20]. Therefore, the amount and the frequency of the absorbed light and the total reflected or transmitted light can be used to infer the chemical composition of the sample.…”