Near-Infrared (NIR) Spectroscopy in Postharvest Quality Assessment of Fruits and Vegetables

Postharvest quality management of fruits and vegetables is crucial for maintaining the shelf life, nutritional content, and consumer appeal of fresh produce. Near-infrared (NIR) spectroscopy has emerged as a non-destructive, rapid, and reliable tool for assessing the internal and external qualities of fruits and vegetables during the postharvest phase (Williams and Norris, 2001). This technology enables real-time monitoring of various quality parameters such as moisture content, sugar concentration, firmness, ripeness, and the presence of defects or diseases. The ability of NIR spectroscopy to measure these attributes quickly and non-invasively makes it highly valuable for the postharvest processing industry.

Principles of NIR Spectroscopy

NIR spectroscopy operates in the wavelength range of 780 to 2500 nanometers. This region of the electromagnetic spectrum corresponds to the overtones and combinations of fundamental vibrations primarily associated with molecular bonds such as O-H, C-H, and N-H. These bonds are abundant in organic compounds, which is why NIR spectroscopy is highly sensitive to components like water, carbohydrates, proteins, and fats.

In fruits and vegetables, water content, sugars, and other compounds can absorb NIR light at specific wavelengths, producing characteristic absorption patterns or spectra. By analyzing these spectra, NIR spectroscopy can provide information about the chemical composition and physical properties of produce without damaging it.

Key Applications in Postharvest Quality Assessment

1. Moisture Content Measurement

Water is one of the most critical quality parameters for fruits and vegetables as it affects texture, firmness, and shelf life. Dehydration, a common issue in postharvest processes, can lead to weight loss, wilting, and poor texture, impacting both the quality and market value of the product. NIR spectroscopy can measure moisture content by detecting the absorption of light caused by O-H bonds in water molecules. This capability is especially useful for monitoring dehydration or water loss during storage or transport.

Example: NIR spectroscopy has been successfully applied to measure moisture content in various fruits such as apples, oranges, and bananas. For instance, NIR is used in citrus processing to monitor moisture loss, which is a critical factor in maintaining juiciness and texture.

2. Determination of Sugar Content (Brix Measurement)

Sugar content, commonly expressed as Brix, is an important indicator of the sweetness and ripeness of fruits. Brix is a measure of the soluble solids in the fruit, predominantly sugars such as glucose, fructose, and sucrose. Higher sugar content generally correlates with better flavor and consumer acceptance. Traditionally, sugar content has been measured by destructive methods, such as refractometry, which require samples to be physically extracted. NIR spectroscopy offers a non-destructive alternative by analyzing the absorption patterns of sugars in the fruit.

Example: In the citrus industry, NIR spectroscopy is widely used to measure the Brix level in oranges, helping determine the optimal harvest time and ensuring high-quality juice production. It has also been used in apples and peaches to evaluate sweetness levels during ripening and storage.

3. Firmness and Ripeness Assessment

Firmness is a crucial quality parameter for fruits and vegetables, as it directly affects texture and consumer preference. As fruits ripen, they undergo softening due to the breakdown of cell walls and pectins. Monitoring firmness allows producers to evaluate ripeness and predict shelf life. NIR spectroscopy can assess fruit firmness by correlating the absorbance of certain wavelengths with changes in cell structure and water content during the ripening process.

Example: NIR spectroscopy has been used to assess the firmness of apples and peaches during storage. In these applications, the technology helps predict the optimal time for consumption or processing. It has also been used to monitor the ripeness of tomatoes and avocados, fruits known for their rapid softening post-harvest.

4. Internal Quality Control (Defect Detection)

The internal quality of fruits and vegetables is difficult to assess visually. Internal defects such as bruising, decay, or hollow cavities can significantly reduce the quality and marketability of produce. Traditional methods for detecting internal defects are destructive, often requiring cutting the fruit open. NIR spectroscopy, however, allows for non-destructive internal inspection by measuring how light penetrates and reflects within the tissue. Any inconsistencies in internal composition, such as those caused by bruising or disease, can be detected by changes in the NIR spectra.

Example: In apples, NIR spectroscopy has been used to detect internal browning and watercore (a disorder affecting the fruit’s flesh). Similarly, NIR technology has been applied to potatoes to detect internal defects like black spots or hollow heart, a common postharvest issue in the potato industry. This enables quality control systems to remove defective produce from the supply chain before it reaches consumers.

5. Ripeness and Maturity Classification

Determining the optimal harvest time is critical for maximizing the quality of fruits and vegetables. Harvesting too early can result in poor flavor and texture, while harvesting too late can lead to over-ripeness and spoilage. NIR spectroscopy can assess ripeness by measuring changes in key chemical compounds such as sugars, organic acids, and pigments. These changes are associated with the ripening process and produce characteristic NIR absorption spectra that can be analyzed to classify produce by ripeness stage. The application is still considered difficult to apply and judge because of the nature of using correlations with maturity and the quality of the predictive power of models in use (Lu & Ariana, 2002; Gomez et al., 2006; Zude et al., 2006).

Example: NIR spectroscopy has been applied to classify mangoes, avocados, and bananas by ripeness stage. By monitoring changes in sugar content, starch breakdown, and pigment levels, NIR can accurately predict the optimal harvest window, ensuring that the fruit reaches consumers at peak quality.

6. Chlorophyll and Color Measurement

Color is an important quality factor for fruits and vegetables, influencing consumer choice and perceived freshness. Chlorophyll, carotenoids, and anthocyanins are the primary pigments responsible for the color of fruits and vegetables. These pigments absorb light in the NIR and visible regions, providing valuable information about the ripeness, freshness, and senescence of produce.

Example: NIR spectroscopy has been used to monitor the degradation of chlorophyll in leafy greens, such as spinach, during storage. As chlorophyll breaks down, the characteristic absorption of light in the NIR region decreases, allowing NIR technology to predict when greens will lose their vibrant color. Similarly, NIR has been used to monitor the ripening process in tomatoes by tracking the increase in red pigments (lycopene) and the corresponding decrease in green chlorophyll.

7. Measurement of Acidity and pH

Acidity is another important factor in the flavor profile and shelf life of fruits and vegetables. pH and titratable acidity influence taste and can also impact microbial stability during storage. Traditionally, these attributes are measured through chemical analysis, but NIR spectroscopy provides a non-invasive alternative by detecting the absorbance of organic acids.

Example: NIR has been employed to monitor the acidity in citrus fruits, such as lemons and oranges, where the balance of sugar and acid is critical to flavor. It is also used to assess acidity in berries, tomatoes, and grapes, ensuring that the produce meets the desired flavor profile before processing or consumption.

Benefits of NIR Spectroscopy in Postharvest Quality Assessment

1. Non-Destructive Measurement: One of the primary advantages of NIR spectroscopy is that it allows for the non-destructive measurement of quality parameters. This means that the same sample can be measured multiple times over its shelf life, providing dynamic data on how quality changes over time.
2. Speed and Efficiency: NIR spectroscopy is a rapid analytical tool, capable of providing real-time data in seconds. This makes it highly efficient for large-scale operations such as sorting, grading, and quality control in postharvest processing lines.
3. Multivariate Analysis: NIR spectroscopy allows for the simultaneous measurement of multiple quality attributes, such as moisture content, sugar concentration, and acidity. This multivariate capability enhances the overall efficiency of quality control processes.
4. Automation and Integration: NIR sensors can be integrated into automated systems for continuous quality monitoring. For example, NIR spectroscopy can be embedded in sorting machines to automatically grade fruits and vegetables based on internal and external quality characteristics.
5. Reduced Waste: By detecting internal defects and optimizing harvest timing, NIR spectroscopy helps reduce food waste in the supply chain. Early detection of quality issues allows producers to take corrective action before the produce reaches consumers.

Challenges and Limitations of NIR Spectroscopy

1. Calibration Requirements: NIR spectroscopy requires calibration models to interpret the spectral data accurately. Developing these models can be time-consuming and may require large datasets to cover the full range of product variability.
2. Surface Effects: NIR spectroscopy is sensitive to surface properties, such as skin thickness or texture. Variations in surface characteristics can affect the accuracy of measurements, particularly for internal quality attributes.
3. Limited Penetration Depth: NIR light can only penetrate a few millimeters into the sample, which may limit its ability to detect internal defects deep within the fruit or vegetable.

NIR Technology Applications

NIR spectroscopy is used mainly in portable systems where postharvest measurements are concerned and as part of on-line grading systems. The measures are many and varied. The most successful applications in the real world are to monitor for damage and defects alongside soluble solids content (SSC). 

Pattern recognition techniques are becoming more sophisticated. A number of equipment suppliers offer systems that combine spectroscopy with multivariate data analysis techniques including PLS (Partial Least Squares Regression Analysis) and artificial neural networks (ANN) (Nicolai et al., 2007). The methods that exploit discriminant technology include  (LDA) linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA) and in recent times SIMCA.

Apricots

A typical example of the use of NIR refers to the assessment of apricots which are underrepresented in the literature because of the interest in more common fruits such as apples. In one study diffuse reflectance spectra was collected in  the 12000 to 4000 cm⁻¹ range with 8 cm⁻¹ of resolution) with an average of 32 scans, in relation to a reflectance standard (Spectralone) (Berardinelli et al., 2010). The fibre optic sampling probe was an IN 261 (Bruker Optics, Mass., USA) and placed in direct contact with the fruit. The head diameter was 10 mm. This was connected to an FT-NIR spectrometer (MATRIX TM -F, Bruker Optics). They used a bifurcated fibre bundle to illuminate the sample and collect the diffusely scattered light.

NIR spectroscopy has proven to be an invaluable tool in postharvest processes, offering a fast, non-destructive means of assessing the quality of fruits and vegetables. Its applications range from measuring moisture content and sugar levels to detecting internal defects and ripeness. With the ability to integrate into automated sorting and grading systems, NIR spectroscopy can significantly enhance the efficiency and accuracy of postharvest quality control, leading to better products, reduced waste, and higher consumer satisfaction.

References

Bureau, S., Ruiz, D., Reich, M., Gouble B, Bertrand D, Audergon J-M, Renard CMGC. (2009). Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy. Food Chem. 113 pp. 1323–8

Camps, C., Christen, D. (2009). Non-destructive assessment of apricot fruit quality by portablevisible-near infrared spectroscopy. LWT Food Sci Technol. 42 pp. 1125–31

Gómez, A.H., He, Y., Pereira, A.G. (2006). Non-destructive measurement of acidity, soluble solids and firmness of Satsuma mandarin using Vis/NIR-spectroscopy techniques. J Food Eng. 77 pp. 313–9

Liu, L., Cozzolino, D., Cynkar, W.U., Dambergs, R.G., Janik, L., O’Neill BK, Colby CB, Gishen M.2008. Preliminary study in the application of visible-near infrared spectroscopy and chemo-metrics to classify Riesling wines from different countries. Food Chem. 106 pp. 781–6.

Lu, R., Ariana, D. (2002). A near-infrared sensing technique for measuring internal quality of apple fruit. Appl Eng Agric 18 pp. 585–90.

Nicolai, B.M., Beullens, K., Bobelyn, E., Peirs, A., Saeys, W., Theron, K.I., Lammertyn J. (2007). Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: a review. Postharv. Biol. Technol. 46 pp. 99–118.

Williams, P.C., Norris, K. (2001). Near-infrared technology in the agricultural and food industry. St. Paul, Minn.: American Assoc. of Cereal Chemists Inc.

Zude, M., Herold, B, Roger J-M, Bellon-Maurel V, Landahl S. (2006). Non-destructive tests on the prediction of apple fruit flesh firmness and soluble solids content on tree and in shelf life. J Food Eng 77 pp. 254–60