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Pears and Pruning: Non-Destructive Dry Matter Measurement in Pears

Posted by: Scott Trimble
Dec. 14, 2020

Dry matter has been gaining popularity as a quality metric in fruit production to fix harvest time and predict postharvest quality and taste. There is a renewal of research in many fruits to optimize fruit quality and yield using this new metric. Though these developments, themselves, are exciting, new technologies to measure dry matter are just as crucial in expanding its use in research and the field.

Measuring Pruning Benefits in Pear Trees

Dry matter (DM) content at harvest is a good predictor of postharvest quality and is, therefore, being increasingly favored over the estimation of soluble sugar content (SSC) as a fruit metric. Research initially focused on finding the optimum levels of dry matter content at harvest time, which could lead to proper ripening and meet consumer taste preferences.
 
The focus has now shifted to fine-tuning several agricultural practices to increase dry matter in fruits. Since dry matter is the total of all solids in fruits—such as starches, sugars, proteins, structural carbohydrates, pigments, vitamins, etc.—minus its water content, all solids accumulated will increase dry matter content. Hence, texture, flavor, and sweetness are linked to DM content, and fruits with high DM fetch higher prices in the market.

Most pear orchards in the USA use traditional systems. These comprise of low to medium density non-trellised planting. Grafts on pears known for robust growth produce dense canopies with varying light penetration into the foliage.

Leaves need light to manufacture the compounds that accumulate in fruits, as they act as sinks during fruit development. It is also well known that fruit exposure to light can enhance taste attributes and lead to higher accumulation of sugars and, thus, DM.

Several orchard management practices can be altered to improve fruit quality to meet consumer taste preferences. Scientists Alex Goke, Sara Serra, and Stefano Musacchi, from the Tree Fruit Research & Extension Center, at Washington State University, studied the effect of pruning to alter the leaf canopy for light management and decrease crop load towards improving fruit size and DM content.  

They wanted to study the effects of the interaction of environment/resource availability and inter-organ competition between various sinks for DM. The timing of pruning can also be crucial, as it will affect the physiology of the tree differently. The common timing for pruning is in winter or fall. Scientists wanted to study the effect of an additional second pruning in summer to control vegetative vigor to prioritize fruit quality. So, Goke et al. tested winter and fall pruning with summer cuts. They thinned older spurs, as this is where most pears are borne. They monitored vegetative growth, bloom, and yield across two seasons.

DM content estimation provides people an easy way to track the results of pruning on fruits at harvest and during storage. However, the traditional methods of DM estimation are not useful in this case.

Problems with Standard DM Estimation

The following are the most common and standard means of dry matter estimation in research:

  1. Oven or micro-oven drying method at 100 degrees Celsius or 70 degrees Celsius is time-consuming, as samples have to be dried for 48 hours. Moreover, this technique is not as precise as toluene distillation, and there are chances of underestimation of dry matter.
  2. Freeze drying similarly requires an experimental time of 48 hours.
  3. Toluene distillation—both corrected and uncorrected—is considered to be the most accurate of all the systems, but it requires chemicals like acids, ammonia, and ethanol. Distillation requires time and attention to get precise results.


There are several disadvantages of the drying processes:

  • All drying techniques lead to loss of dry matter during the experiment, altering the accuracy of the results, so the amount of dry matter content in fruit can be under-reported. These losses are more pronounced in oven drying than in freeze drying.
  • Both oven and freeze drying are not able to account for the loss of volatile compounds during the drying process.

In all these cases, the time needed to prepare and analyze 1,200 to 2,000 fruit samples needed in the pear experiment would be considerable. Moreover, these techniques were destructive.

Solution: Non-destructive Portable NIR Spectrometers

The scientists wanted a DM estimation method that was non-destructive, rapid, precise, and easy to use. They chose to use a near-infrared spectrometer, the F-750 Produce Quality Meter, produced by Felix Instruments Applied Food Science.

Near-Infrared Spectroscopy can estimate DM content accurately and non-destructively. However, most laboratory versions are very expensive, large, and require skilled operators, as well as sample preparation. In contrast, the F-750 is a small, handheld portable device that contains miniaturized NIR spectroscopy.  

The availability of portable yet precise tools like the F-750 makes analyzing DM simple. Besides DM, the F-750 can also record SSC, titrable acidity, and external and internal color.

The scientists monitored flowering and the fruit set on the east and west side of 957 trees in the orchards. After harvest, pear samples were collected from each tree and a subset of 500 pears/treatment in 2016 and 300 pears/treatment in 2017. As there were four treatments—fall pruning, winter pruning, fall+summer pruning, and winter+summer pruning—2,000 pears and 1,200 pears were tested in 2016 and 2017, respectively.  

Though the F-750 already has ready models for pears, the scientists decided to design specific predictive models for the d’Anjou Pear variety tested in the experiments. The F-750 has a model building software with user-friendly instructions that make it easy to customize the tools for varieties. The resultant new models were highly accurate and showed only a 0.29 and 0.34 root mean squared error (RMSE).

The F-750 was used for the initial mass testing of DM in thousands of pears to divide them into three classes of low, medium, and high DM content.

Subsequently, the harvested fruits were stored at industry conditions around 1 degree Celsius and ripened for seven days at ≈23 degrees Celsius to achieve consumer eating quality, each time before testing. The subsequent DM estimation was done by oven-drying methods at 60 degrees Celsius, using only 3-7 pears for each treatment.

Figure 1. “Mean firmness (kg, n = 487 and 350 in 2016 and 2017, respectively), soluble solids content (SSC, °Brix, n = 487 and 347 in 2016 and 2017, respectively), destructively measured actual dry matter (%, n = 108 and 233 in 2016 and 2017, respectively). and acidity (% malic acid, n = 64 and 60 in 2016 and 2017, respectively) for d’Anjou fruit evaluated one mo. postharvest (within one month after harvest in cold storage plus seven days of ripening at room temperature) in 2016 (left) and 2017 (right) among seasonal pruning treatments consisting of fall pruning (F, orange), fall and summer pruning (F+S, yellow), winter pruning (W, blue), and winter and summer pruning (W+S, green). Error bars indicate ± standard error. Different letters indicate significance difference among means (p < 0.05, Tukey HSD). “ns” indicates no significant difference among means (p > 0.05, Tukey HSD,” Goke et al. 2020. (Image credits: Agronomy 2020, 10(6), 897; https://doi.org/10.3390/agronomy10060897)

Benefits of Using NIR Spectrometers

The scientists found the whole procedure of DM estimation with F-750 fast and easy, leaving them time to tackle other tasks in their research program.

The fruit is placed on the lens opening and the metric is analyzed within 4-5 seconds; the value is simultaneously stored on the instrument. The battery of the device can analyze and record 1,600+ measurements for one charge. Hence, the scientists could complete the processing of 1,200-2,000 samples within a day or two.

The F-750 stores the measurements on a 32 GB SD card with which data can be easily transferred to a computer for statistical analysis. Therefore, the scientists could also save time in data entry.

Since the F-750 estimation method was non-destructive, the scientists could conduct repeated experiments with the same fruits within one month after harvest and then five months after the harvest.

Moreover, the same fruits were tested for SSC, firmness, peel color, and index of absorbance difference (IAD), as shown in Figure 1.

Pruning Effectiveness and Season


Figure 2. Mean proportion of low (<13%), moderate (13–16%), and high (>16%) predicted dry matter classes of pears determined at harvest in 2016 (upper), 2017 (lower) among seasonal pruning treatments consisting of fall pruning (F, orange, n = 512 and 320 in 2016 and 2017, respectively), fall and summer pruning (F+S, yellow, n = 501 and 320 in 2016 and 2017, respectively), winter pruning (W, blue, n = 500 and 320 in 2016 and 2017, respectively), and winter and summer pruning (W+S, green, n = 500 and 320 in 2016 and 2017, respectively). Error bars indicate ± standard error. Different letters indicate significant difference among means (p < 0.05, Tukey HSD). “ns” indicates no significant difference among means (p > 0.05, Tukey HSD).

Having a non-destructive DM estimation method for the first step was vital to the whole experiment, as the same fruits were measured several times to estimate several quality parameters.

The F-750 was used to measure the DM content of fruits at harvest. The fruits were divided into three classes: pears with low (<13%), moderate (13–16%), and high (>16%) dry matter content. The performance of these groups was then tracked throughout the postharvest testing period.

Adding summer pruning to winter and fall pruning was beneficial in the case of d’Anjou pear to increase fruit size. A maximum fruit size increase of 13% was achieved, producing pears that were 80 mm bigger in diameter.

However, summer pruning also decreased yield/ tree by 30 kg and lowered the average DM content by 0.5%. The proportion of fruits with high DM content (>16%) was also lowered by 11%, as shown in Figure 2.

Fall pruning was better than winter pruning. Fall pruned pears had higher DM content at harvest of 15.1 and 13.7 in 2016 and 2017, respectively, while winter-pruned pears had only 14.2% and 13.1% in 2016 and 2017, respectively.

The experiment proved once again that DM is a reliable predictor of postharvest quality. The three different DM classes allowed the scientists to prove that higher DM was consistently associated with better SSC or sweetness postharvest after many months of cold storage.

Conclusion

The existence of the DM quality metric and its easy measurement with the F-750 made it possible for the scientists to find an efficient way of finding the best orchard management practices to optimize consumer satisfaction. The study showed that small differences in orchard management in pears are related to DM accumulation that can produce significant differences in the ROI of a farm. Besides finding the best practices, the scientists have also shown growers how they can track the success of orchard management to quality and yield of pears.

By combining targeted seasonal pruning with precise measurement of DM at harvest, growers can improve the yield and quality of pears. The built-in GPS in the F-750 device and its compatibility with Fruit Maps and other harvest mapping software can help in harvesting at the correct time. The F-750 is simple and is designed for use throughout the fresh produce supply chain, from farms to retail shops. Hence, pear growers and other stakeholders can immediately take advantage of the research by Goke and his associates.

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Vijayalaxmi Kinhal
Science Writer, CID Bio-Science
Ph.D. Ecology and Environmental Science, B.Sc Agriculture

Feature image courtesy of Laura K

Sources

Brahmakshatriya, R.D., & Donker J.D. (1970). Five Methods for Determination of Silage Dry Matter.
Goke, A., Serra, S., & Musacchi, S. (2020). Manipulation of Fruit Dry Matter via Seasonal Pruning and Its Relationship to d’Anjou Pear Yield and Fruit Quality. Agronomy, 10(6), 897. https://doi.org/10.3390/agronomy10060897897.


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Scott Trimble

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strimble@cid-inc.com